Descriptive stats
# packages -----
library(dplyr)
#library(bshazard)
library(ggplot2)
library(tidyverse)
library(ggalt)
library(survminer)
library(ggridges)
library(tableone)
library(kableExtra)
library(stringr)
library(scales)
library(knitr)
library(survminer)
#library(ggcorrplot)
library(cmprsk)nice.num<-function(x){
prettyNum(x, big.mark=",", nsmall = 0, digits=0,scientific = FALSE)}
nice.num2<-function(x){
prettyNum(x, big.mark=",", nsmall = 2, digits=2,scientific = FALSE)}Table 1: Patient characteristics
Patient characteristics by state (general population) or transition (from general population, diagnosed with COVID-19, and hospitalised with COVID-19)
table1.data<-rbind(
# all those in general population
r.healthy.diagnosis%>%
mutate(group="General population"),
# all those who transition from general population to diagnosed
r.healthy.diagnosis%>%
filter(status==1) %>%
mutate(group="From general population to diagnosed with COVID-19"),
# all those who transition from general population to hospitalised
r.healthy.hospitalised%>%
filter(status==1) %>%
mutate(group="From general population to hospitalised with COVID-19"),
# all those who transition from diagnosed to hospitalised
r.diagnosis.hospitalised %>%
filter(status==1) %>%
mutate(group="From diagnosed with COVID-19 to hospitalised with COVID-19"),
# all those who transition from hospitalised to death
r.hospitalised.death %>%
filter(status==1) %>%
mutate(group="From hospitalised with COVID-19 to death"),
# # all those who transition from general population to death
r.healthy.death %>%
filter(status==1) %>%
mutate(group="From general population to death"),
# all those who transition from diagnosed to death
r.diagnosis.death %>%
filter(status==1) %>%
mutate(group="From diagnosed with COVID-19 to death")) %>%
mutate(group=factor(group,
levels=c("General population",
"From general population to diagnosed with COVID-19",
"From general population to hospitalised with COVID-19",
"From general population to death",
"From diagnosed with COVID-19 to hospitalised with COVID-19",
"From diagnosed with COVID-19 to death",
"From hospitalised with COVID-19 to death"
))) %>%
mutate(gender=factor(gender,
levels=c("Male", "Female")))
# variables for table 1
vars<-c("age",
"age_gr",
"gender",
"charlson",
# components of charlson
"Myocardial_infarction",
"Congestive_heart_failure",
"Peripheral_vascular_disease",
"Cerebrovascular_disease",
"Dementia",
"Chronic_pulmonary_disease",
"Rheumatologic_disease",
"Peptic_ulcer_disease",
"Mild_liver_disease",
"Diabetes_with_chronic_complications",
"Hemoplegia_or_paralegia",
"Renal_disease",
"Any_malignancy",
"Moderate_to_severe_liver_disease",
"Metastatic_solid_tumor",
"AIDS",
# atlas cohorts
"a_autoimmune_condition",
"a_chronic_kidney_disease",
"a_copd",
"a_dementia",
"a_heart_disease",
"a_hyperlipidemia",
"a_hypertension",
"a_malignant_neoplasm",
"a_obesity.5y",
"a_t2_diabetes")
factor.vars<- c("age_gr",
"gender",
"charlson",
"Myocardial_infarction",
"Congestive_heart_failure",
"Peripheral_vascular_disease",
"Cerebrovascular_disease",
"Dementia",
"Chronic_pulmonary_disease",
"Rheumatologic_disease",
"Peptic_ulcer_disease",
"Mild_liver_disease",
"Diabetes_with_chronic_complications",
"Hemoplegia_or_paralegia",
"Renal_disease",
"Any_malignancy",
"Moderate_to_severe_liver_disease",
"Metastatic_solid_tumor",
"AIDS",
"a_autoimmune_condition",
"a_chronic_kidney_disease",
"a_copd",
"a_dementia",
"a_heart_disease",
"a_hyperlipidemia",
"a_hypertension",
"a_malignant_neoplasm",
"a_obesity.5y",
"a_t2_diabetes" )
summary.characteristics<-print(CreateTableOne(
vars = vars,
factorVars = factor.vars,
includeNA=T,
data = table1.data,
strata=c("group"),
test = F),
showAllLevels=F,smd=F,
nonnormal = vars, #all
noSpaces = TRUE,
contDigits = 1,
printToggle=FALSE)
# format numbers (eg commas etc)
# functionality does not seem to be in tableone package
# so do this manually
for(i in 1:ncol(summary.characteristics)) {
# tidy up
cur_column <- summary.characteristics[, i]
cur_column <- str_extract(cur_column, '[0-9.]+\\b') %>%
as.numeric()
cur_column <-nice.num(cur_column)
# add back in
summary.characteristics[, i] <- str_replace(string=summary.characteristics[, i],
pattern='[0-9.]+\\b',
replacement=cur_column)
}
# names
#rownames(summary.characteristics)
rownames(summary.characteristics)<-str_replace(rownames(summary.characteristics), "charlson", "charlson comorbidity index")
rownames(summary.characteristics)<-str_replace(rownames(summary.characteristics), "obesity.5y", "obesity")
rownames(summary.characteristics)<-str_replace(rownames(summary.characteristics), " = 1", "")
rownames(summary.characteristics)<-str_replace(rownames(summary.characteristics), "a_", "")
rownames(summary.characteristics)<-str_replace(rownames(summary.characteristics) , "_", " ")
rownames(summary.characteristics)<-str_replace(rownames(summary.characteristics) , "_", " ")
rownames(summary.characteristics)<-str_replace(rownames(summary.characteristics) , "t2", "Type 2")
rownames(summary.characteristics)<-str_replace(rownames(summary.characteristics) , "copd", "COPD")
rownames(summary.characteristics)<-str_to_sentence(rownames(summary.characteristics))# obscure any counts less than 5
summary.characteristics<-apply(summary.characteristics, 2,
function(x)
ifelse(str_sub(x, 1, 2) %in% c("0 ", "1 ","2 ", "3 ","4 "),
"<5",x))
kable(summary.characteristics,
col.names = c("General population",
"To diagnosed with COVID-19",
"To hospitalised with COVID-19",
"To death",
"To hospitalised with COVID-19",
"To death",
"To death")) %>%
add_header_above(c(" "=1,
"From general population"= 3,
"From diagnosed with COVID-19"= 2,
"From hospitalised with COVID-19"= 1)) %>%
kable_styling(bootstrap_options = c("striped", "bordered")) %>%
pack_rows("Components of Charlson Comorbidity Index", 16, 32) %>%
pack_rows("Other conditions of interest", 33, 41)| General population | To diagnosed with COVID-19 | To hospitalised with COVID-19 | To death | To hospitalised with COVID-19 | To death | To death |
|---|---|---|---|---|---|---|
| 5,627,520 | 109,367 | 8,582 | 11,726 | 9,437 | 2,794 | 2,791 |
| 44 [25.0, 60.0] | 47 [36.0, 61.0] | 72 [58.2, 81.0] | 85 [76.0, 90.0] | 61 [50.0, 75.0] | 87 [81.0, 91.0] | 81 [73.0, 86.5] |
| 967,227 (17.2) | 4,547 (4.2) | 34 (0.4) | 9 (0.1) | 40 (0.4) | <5 | <5 |
| 1,438,732 (25.6) | 30,640 (28.0) | 432 (5.0) | 80 (0.7) | 857 (9.1) | 5 (0.2) | 11 (0.4) |
| 1,788,832 (31.8) | 44,803 (41.0) | 1,815 (21.1) | 658 (5.6) | 3,477 (36.8) | 63 (2.3) | 143 (5.1) |
| 617,929 (11.0) | 10,661 (9.7) | 1,590 (18.5) | 1,034 (8.8) | 1,708 (18.1) | 136 (4.9) | 296 (10.6) |
| 474,369 (8.4) | 7,163 (6.5) | 2,252 (26.2) | 1,999 (17.0) | 1,702 (18.0) | 421 (15.1) | 840 (30.1) |
| 340,431 (6.0) | 11,553 (10.6) | 2,459 (28.7) | 7,946 (67.8) | 1,653 (17.5) | 2,169 (77.6) | 1,500 (53.7) |
| 2,859,274 (50.8) | 64,473 (59.0) | 3,770 (43.9) | 5,932 (50.6) | 4,357 (46.2) | 1,625 (58.2) | 1,122 (40.2) |
| 4,572,265 (81.2) | 81,833 (74.8) | 3,750 (43.7) | 1,672 (14.3) | 5,513 (58.4) | 391 (14.0) | 604 (21.6) |
| 410,497 (7.3) | 10,708 (9.8) | 1,156 (13.5) | 1,724 (14.7) | 1,157 (12.3) | 566 (20.3) | 447 (16.0) |
| 363,057 (6.5) | 8,211 (7.5) | 1,518 (17.7) | 2,398 (20.5) | 1,266 (13.4) | 507 (18.1) | 572 (20.5) |
| 281,701 (5.0) | 8,615 (7.9) | 2,158 (25.1) | 5,932 (50.6) | 1,501 (15.9) | 1,330 (47.6) | 1,168 (41.8) |
| Components of Charlson Comorbidity Index | ||||||
| 56,855 (1.0) | 1,238 (1.1) | 364 (4.2) | 687 (5.9) | 249 (2.6) | 137 (4.9) | 166 (5.9) |
| 74,650 (1.3) | 2,574 (2.4) | 743 (8.7) | 2,383 (20.3) | 454 (4.8) | 450 (16.1) | 424 (15.2) |
| 65,165 (1.2) | 1,576 (1.4) | 505 (5.9) | 1,143 (9.7) | 303 (3.2) | 191 (6.8) | 250 (9.0) |
| 69,629 (1.2) | 2,229 (2.0) | 434 (5.1) | 1,097 (9.4) | 343 (3.6) | 281 (10.1) | 238 (8.5) |
| 58,138 (1.0) | 4,917 (4.5) | 608 (7.1) | 2,972 (25.3) | 490 (5.2) | 1,154 (41.3) | 423 (15.2) |
| 315,854 (5.6) | 8,074 (7.4) | 1,349 (15.7) | 2,354 (20.1) | 1,047 (11.1) | 473 (16.9) | 593 (21.2) |
| 46,567 (0.8) | 1,233 (1.1) | 274 (3.2) | 429 (3.7) | 184 (1.9) | 90 (3.2) | 114 (4.1) |
| 104,894 (1.9) | 2,657 (2.4) | 412 (4.8) | 739 (6.3) | 397 (4.2) | 164 (5.9) | 157 (5.6) |
| 53,311 (0.9) | 1,264 (1.2) | 191 (2.2) | 444 (3.8) | 177 (1.9) | 49 (1.8) | 75 (2.7) |
| 76,214 (1.4) | 1,932 (1.8) | 562 (6.5) | 980 (8.4) | 372 (3.9) | 220 (7.9) | 259 (9.3) |
| 9,995 (0.2) | 432 (0.4) | 64 (0.7) | 184 (1.6) | 49 (0.5) | 60 (2.1) | 46 (1.6) |
| 204,825 (3.6) | 5,839 (5.3) | 1,581 (18.4) | 3,835 (32.7) | 1,072 (11.4) | 953 (34.1) | 864 (31.0) |
| 354,645 (6.3) | 8,526 (7.8) | 1,783 (20.8) | 4,282 (36.5) | 1,397 (14.8) | 736 (26.3) | 825 (29.6) |
| 8,673 (0.2) | 181 (0.2) | 61 (0.7) | 163 (1.4) | 40 (0.4) | 11 (0.4) | 31 (1.1) |
| 8,732 (0.2) | 185 (0.2) | 60 (0.7) | 530 (4.5) | 34 (0.4) | 31 (1.1) | 30 (1.1) |
| 18,226 (0.3) | 421 (0.4) | 42 (0.5) | 44 (0.4) | 48 (0.5) | <5 | <5 |
| 277,450 (4.9) | 6,936 (6.3) | 871 (10.1) | 1,320 (11.3) | 786 (8.3) | 291 (10.4) | 340 (12.2) |
| Other conditions of interest | ||||||
| 211,842 (3.8) | 5,843 (5.3) | 1,548 (18.0) | 3,844 (32.8) | 1,058 (11.2) | 955 (34.2) | 858 (30.7) |
| 122,745 (2.2) | 2,889 (2.6) | 801 (9.3) | 1,488 (12.7) | 515 (5.5) | 280 (10.0) | 379 (13.6) |
| 57,310 (1.0) | 4,795 (4.4) | 598 (7.0) | 2,894 (24.7) | 486 (5.1) | 1,108 (39.7) | 416 (14.9) |
| 547,795 (9.7) | 13,540 (12.4) | 2,846 (33.2) | 5,865 (50.0) | 2,132 (22.6) | 1,264 (45.2) | 1,371 (49.1) |
| 520,837 (9.3) | 11,888 (10.9) | 1,590 (18.5) | 1,630 (13.9) | 1,599 (16.9) | 415 (14.9) | 523 (18.7) |
| 703,167 (12.5) | 16,400 (15.0) | 2,609 (30.4) | 3,971 (33.9) | 2,412 (25.6) | 970 (34.7) | 969 (34.7) |
| 297,409 (5.3) | 6,656 (6.1) | 1,405 (16.4) | 3,818 (32.6) | 1,075 (11.4) | 626 (22.4) | 691 (24.8) |
| 937,593 (16.7) | 22,493 (20.6) | 3,384 (39.4) | 3,251 (27.7) | 3,286 (34.8) | 733 (26.2) | 1,118 (40.1) |
| 324,944 (5.8) | 7,427 (6.8) | 1,773 (20.7) | 2,684 (22.9) | 1,422 (15.1) | 637 (22.8) | 734 (26.3) |
Figure 1: Age and gender histogram
As above, by state/ transition, transition 3 (general pop to death omitted as not of primary interest)
plot.data<-table1.data
# to split across lines
plot.data$group<-plyr::revalue(plot.data$group, c("From general population to diagnosed with COVID-19"=
"From general population\nto diagnosed with COVID-19",
"From general population to hospitalised with COVID-19"=
"From general population\nto hospitalised with COVID-19",
"From general population to death"=
"From general population\nto death",
"From diagnosed with COVID-19 to hospitalised with COVID-19"=
"From diagnosed with COVID-19\nto hospitalised with COVID-19",
"From diagnosed with COVID-19 to death"=
"From diagnosed with COVID-19\nto death",
"From hospitalised with COVID-19 to death"=
"From hospitalised with COVID-19\nto death"))
#Mirrored
plot.data.male<-plot.data %>%
filter(gender=="Male") %>%
filter(group!="From general population\nto death")
plot.data.female<-plot.data %>%
filter(gender=="Female") %>%
filter(group!="From general population\nto death")
dat_text <- data.frame(
label = c("Female", "Male"),
group = factor(c("General population","General population")),
x = c(75,75),
y = c(-0.04, 0.035)
)
histogram3<-ggplot() +
geom_histogram(data=plot.data.male ,
aes(x=age, y=..density..),
colour="black",
binwidth = 4, boundary = 0,
fill="#F21A00")+
geom_histogram(aes(x=age, y=-..density..),
colour="black",
binwidth = 4, boundary = 0,
data=plot.data.female,
fill="#3B9AB2")+
coord_flip()+
facet_wrap(group ~.)+
theme_bw()+
scale_y_continuous(breaks=c(-0.1,-0.05, 0,0.05,0.1))+
xlim(0,104)+
geom_label(
data = dat_text,
mapping = aes(x = x,
y = y, label = label),
size=5)+
theme(legend.title = element_blank(),
axis.text=element_text(size=12),
axis.title=element_text(size=14,face="bold"),
strip.text = element_text(size=12, face="bold"),
strip.background = element_rect( fill="#f7f7f7"),
legend.text=element_text(size=14),
legend.position="top") +
ylab("Density")+
xlab("Age")
ggsave( "histogram3.png",histogram3,
dpi=300,
width = 11, height = 7)
include_graphics("histogram3.png")
Figure 2: Conditions by age group and state/ tranistion
a<-table1.data %>%
group_by(gender, group) %>%
add_tally() %>%
group_by(gender, group, n) %>%
summarise(a_autoimmune_condition=sum(a_autoimmune_condition==1),
a_chronic_kidney_disease =sum(a_chronic_kidney_disease==1),
a_copd =sum(a_copd==1),
a_dementia =sum(a_dementia==1),
a_heart_disease =sum(a_heart_disease==1),
a_hyperlipidemia =sum(a_hyperlipidemia==1),
a_hypertension =sum(a_hypertension==1),
a_malignant_neoplasm = sum(a_malignant_neoplasm==1),
a_obesity.5y = sum(a_obesity.5y==1),
a_t2_diabetes =sum(a_t2_diabetes==1)) %>%
mutate(
a_autoimmune_condition=a_autoimmune_condition/n,
a_chronic_kidney_disease =a_chronic_kidney_disease/n,
a_copd =a_copd/n,
a_dementia =a_dementia/n,
a_heart_disease =a_heart_disease/n,
a_hyperlipidemia =a_hyperlipidemia/n,
a_hypertension =a_hypertension/n,
a_malignant_neoplasm = a_malignant_neoplasm/n,
a_obesity.5y = a_obesity.5y/n,
a_t2_diabetes = a_t2_diabetes/n) %>%
pivot_longer(
cols = starts_with("a_"),
names_to = "var",
values_to = "prop",
values_drop_na = TRUE)
a$group<-plyr::revalue(a$group, c("From general population to diagnosed with COVID-19"=
"From general\npopulation to\ndiagnosed with\nCOVID-19",
"From general population to hospitalised with COVID-19"=
"From general\npopulation\nto hospitalised\nwith COVID-19",
"From general population to death"=
"From general\npopulation\nto death",
"From diagnosed with COVID-19 to hospitalised with COVID-19"=
"From diagnosed\nwith COVID-19\nto hospitalised\nwith COVID-19",
"From diagnosed with COVID-19 to death"=
"From diagnosed\nwith COVID-19\nto death",
"From hospitalised with COVID-19 to death"=
"From hospitalised\nwith COVID-19\nto death")) gg.conditions2<-
a %>%
mutate(var.name=
ifelse(
var=="a_autoimmune_condition",
"Autoimmune condition",
ifelse(
var=="a_chronic_kidney_disease",
"Chronic kidney disease",
ifelse(
var=="a_copd",
"COPD",
ifelse(
var=="a_dementia",
"Dementia",
ifelse(
var=="a_heart_disease",
"Heart disease",
ifelse(
var=="a_hyperlipidemia",
"Hyperlipidemia",
ifelse(
var=="a_hypertension",
"Hypertension",
ifelse(
var=="a_malignant_neoplasm",
"Malignant neoplasm",
ifelse(
var=="a_obesity.5y",
"Obesity",
ifelse(
var=="a_t2_diabetes",
"Type 2 Diabetes Mellitus",NA ))))))))))) %>%
filter(group!="From general\npopulation\nto death") %>%
ggplot()+
facet_grid(gender~group, switch="y")+
geom_bar(aes(var, prop, fill=var.name),
width = 1, colour="grey",
stat="identity", position=position_dodge())+
theme_minimal() +
scale_y_continuous( breaks=c(0.1,0.2,0.3,0.4, 0.5, 0.6),
limits=c(-0.075,0.6))+
scale_fill_manual(values = c("#a6cee3","#1f78b4","#b2df8a","#33a02c",
"#fb9a99","#e31a1c","#fdbf6f","#ff7f00","#cab2d6", "#6a3d9a"))+
theme(panel.spacing = unit(0, "lines"),
legend.title = element_blank(),
strip.text = element_text(size=10, face="bold"),
panel.grid.major.x = element_blank() ,
axis.text.x = element_blank(),
axis.title = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
strip.text.y.left = element_text(angle = 0),
legend.position = "bottom" ) +
geom_text(x = 3, y = 0.3,
size=3,
label = "30%")+
geom_text(x = 4, y = 0.6,
size=3,
label = "60%")+
coord_polar(start = 0)
ggsave("conditions2.png",gg.conditions2,
dpi=300,
width = 12, height = 7)
include_graphics("conditions2.png")
Table 2: Transitions
# events
events<-rbind(
# overall
r %>%
group_by(trans) %>%
add_tally() %>%
group_by(trans, n) %>%
summarise(events=sum(status),
time.0=min(time),
time.25=quantile(time, probs =0.25),
time.50=quantile(time, probs =0.5),
time.75=quantile(time, probs =0.75),
time.1=max(time)) %>%
mutate(time=paste0(time.50, " (", time.0, ", ", time.25, " to ", time.75, ", ", time.1, ")")) %>%
mutate(group="all") %>%
mutate(level="all") %>%
select(trans, group,level, n, time, events),
# by age_gr
r %>%
group_by(trans, age_gr) %>%
add_tally() %>%
group_by(trans, n, age_gr) %>%
summarise(events=sum(status),
time.0=min(time),
time.25=quantile(time, probs =0.25),
time.50=quantile(time, probs =0.5),
time.75=quantile(time, probs =0.75),
time.1=max(time))%>%
mutate(age_gr = factor(age_gr, levels = c("Under 18","18 to 39", "40 to 59", "60 to 69",
"70 to 79", "80 or older"))) %>%
arrange(trans, age_gr) %>%
mutate(time=paste0(time.50, " (", time.0, ", ", time.25, " to ", time.75, ", ", time.1, ")")) %>%
mutate(group="Age") %>%
mutate(level=age_gr) %>%
select(trans, group,level, n, time, events),
# by gender
r %>%
group_by(trans, gender) %>%
add_tally() %>%
group_by(trans, n, gender) %>%
summarise(events=sum(status),
time.0=min(time),
time.25=quantile(time, probs =0.25),
time.50=quantile(time, probs =0.5),
time.75=quantile(time, probs =0.75),
time.1=max(time))%>%
mutate(gender = factor(gender, levels = c("Male", "Female"))) %>%
arrange(trans, gender) %>%
mutate(time=paste0(time.50, " (", time.0, ", ", time.25, " to ", time.75, ", ", time.1, ")")) %>%
mutate(group="Sex") %>%
mutate(level=gender) %>%
select(trans, group,level, n, time, events),
# by charlson
r %>%
group_by(trans, charlson) %>%
add_tally() %>%
group_by(trans, n, charlson) %>%
summarise(events=sum(status),
time.0=min(time),
time.25=quantile(time, probs =0.25),
time.50=quantile(time, probs =0.5),
time.75=quantile(time, probs =0.75),
time.1=max(time))%>%
mutate(charlson = factor(charlson, levels = c("0", "1", "2", "3+"))) %>%
arrange(trans, charlson) %>%
mutate(time=paste0(time.50, " (", time.0, ", ", time.25, " to ", time.75, ", ", time.1, ")")) %>%
mutate(group="Charlson") %>%
mutate(level=charlson) %>%
select(trans, group,level, n, time, events),
# by a_autoimmune_condition
r %>%
group_by(trans, a_autoimmune_condition) %>%
add_tally() %>%
group_by(trans, n, a_autoimmune_condition) %>%
summarise(events=sum(status),
time.0=min(time),
time.25=quantile(time, probs =0.25),
time.50=quantile(time, probs =0.5),
time.75=quantile(time, probs =0.75),
time.1=max(time))%>%
mutate(a_autoimmune_condition = factor(a_autoimmune_condition, levels = c("0", "1"))) %>%
arrange(trans, a_autoimmune_condition) %>%
mutate(time=paste0(time.50, " (", time.0, ", ", time.25, " to ", time.75, ", ", time.1, ")")) %>%
mutate(group="Autoimmune condition") %>%
mutate(level=a_autoimmune_condition) %>%
select(trans, group,level, n, time, events),
# by a_chronic_kidney_disease
r %>%
group_by(trans, a_chronic_kidney_disease) %>%
add_tally() %>%
group_by(trans, n, a_chronic_kidney_disease) %>%
summarise(events=sum(status),
time.0=min(time),
time.25=quantile(time, probs =0.25),
time.50=quantile(time, probs =0.5),
time.75=quantile(time, probs =0.75),
time.1=max(time))%>%
mutate(a_chronic_kidney_disease = factor(a_chronic_kidney_disease, levels = c("0", "1"))) %>%
arrange(trans, a_chronic_kidney_disease) %>%
mutate(time=paste0(time.50, " (", time.0, ", ", time.25, " to ", time.75, ", ", time.1, ")")) %>%
mutate(group="Chronic kidney disease") %>%
mutate(level=a_chronic_kidney_disease) %>%
select(trans, group,level, n, time, events),
# by a_copd
r %>%
group_by(trans, a_copd) %>%
add_tally() %>%
group_by(trans, n, a_copd) %>%
summarise(events=sum(status),
time.0=min(time),
time.25=quantile(time, probs =0.25),
time.50=quantile(time, probs =0.5),
time.75=quantile(time, probs =0.75),
time.1=max(time))%>%
mutate(a_copd = factor(a_copd, levels = c("0", "1"))) %>%
arrange(trans, a_copd) %>%
mutate(time=paste0(time.50, " (", time.0, ", ", time.25, " to ", time.75, ", ", time.1, ")")) %>%
mutate(group="COPD") %>%
mutate(level=a_copd) %>%
select(trans, group,level, n, time, events),
# by a_dementia
r %>%
group_by(trans, a_dementia) %>%
add_tally() %>%
group_by(trans, n, a_dementia) %>%
summarise(events=sum(status),
time.0=min(time),
time.25=quantile(time, probs =0.25),
time.50=quantile(time, probs =0.5),
time.75=quantile(time, probs =0.75),
time.1=max(time))%>%
mutate(a_dementia = factor(a_dementia, levels = c("0", "1"))) %>%
arrange(trans, a_dementia) %>%
mutate(time=paste0(time.50, " (", time.0, ", ", time.25, " to ", time.75, ", ", time.1, ")")) %>%
mutate(group="Dementia") %>%
mutate(level=a_dementia) %>%
select(trans, group,level, n, time, events),
# by a_heart_disease
r %>%
group_by(trans, a_heart_disease) %>%
add_tally() %>%
group_by(trans, n, a_heart_disease) %>%
summarise(events=sum(status),
time.0=min(time),
time.25=quantile(time, probs =0.25),
time.50=quantile(time, probs =0.5),
time.75=quantile(time, probs =0.75),
time.1=max(time))%>%
mutate(a_heart_disease = factor(a_heart_disease, levels = c("0", "1"))) %>%
arrange(trans, a_heart_disease) %>%
mutate(time=paste0(time.50, " (", time.0, ", ", time.25, " to ", time.75, ", ", time.1, ")")) %>%
mutate(group="Heart disease") %>%
mutate(level=a_heart_disease) %>%
select(trans, group,level, n, time, events),
# by a_hyperlipidemia
r %>%
group_by(trans, a_hyperlipidemia) %>%
add_tally() %>%
group_by(trans, n, a_hyperlipidemia) %>%
summarise(events=sum(status),
time.0=min(time),
time.25=quantile(time, probs =0.25),
time.50=quantile(time, probs =0.5),
time.75=quantile(time, probs =0.75),
time.1=max(time))%>%
mutate(a_hyperlipidemia = factor(a_hyperlipidemia, levels = c("0", "1"))) %>%
arrange(trans, a_hyperlipidemia) %>%
mutate(time=paste0(time.50, " (", time.0, ", ", time.25, " to ", time.75, ", ", time.1, ")")) %>%
mutate(group="Hyperlipidemia") %>%
mutate(level=a_hyperlipidemia) %>%
select(trans, group,level, n, time, events),
# by a_malignant_neoplasm
r %>%
group_by(trans, a_malignant_neoplasm) %>%
add_tally() %>%
group_by(trans, n, a_malignant_neoplasm) %>%
summarise(events=sum(status),
time.0=min(time),
time.25=quantile(time, probs =0.25),
time.50=quantile(time, probs =0.5),
time.75=quantile(time, probs =0.75),
time.1=max(time))%>%
mutate(a_malignant_neoplasm = factor(a_malignant_neoplasm, levels = c("0", "1"))) %>%
arrange(trans, a_malignant_neoplasm) %>%
mutate(time=paste0(time.50, " (", time.0, ", ", time.25, " to ", time.75, ", ", time.1, ")")) %>%
mutate(group="Malignant neoplasm") %>%
mutate(level=a_malignant_neoplasm) %>%
select(trans, group,level, n, time, events),
# by a_obesity.5y
r %>%
group_by(trans, a_obesity.5y) %>%
add_tally() %>%
group_by(trans, n, a_obesity.5y) %>%
summarise(events=sum(status),
time.0=min(time),
time.25=quantile(time, probs =0.25),
time.50=quantile(time, probs =0.5),
time.75=quantile(time, probs =0.75),
time.1=max(time))%>%
mutate(a_obesity.5y = factor(a_obesity.5y, levels = c("0", "1"))) %>%
arrange(trans, a_obesity.5y) %>%
mutate(time=paste0(time.50, " (", time.0, ", ", time.25, " to ", time.75, ", ", time.1, ")")) %>%
mutate(group="Obesity") %>%
mutate(level=a_obesity.5y) %>%
select(trans, group,level, n, time, events),
# by a_t2_diabetes
r %>%
group_by(trans, a_t2_diabetes) %>%
add_tally() %>%
group_by(trans, n, a_t2_diabetes) %>%
summarise(events=sum(status),
time.0=min(time),
time.25=quantile(time, probs =0.25),
time.50=quantile(time, probs =0.5),
time.75=quantile(time, probs =0.75),
time.1=max(time))%>%
mutate(a_t2_diabetes = factor(a_t2_diabetes, levels = c("0", "1"))) %>%
arrange(trans, a_t2_diabetes) %>%
mutate(time=paste0(time.50, " (", time.0, ", ", time.25, " to ", time.75, ", ", time.1, ")")) %>%
mutate(group="Type 2 diabetes") %>%
mutate(level=a_t2_diabetes) %>%
select(trans, group,level, n, time, events)
)
# format numbers
events$n<-nice.num(events$n)
events$events<-nice.num(events$events)
# order by transition
events<-events %>%
arrange(trans)# factors for covid.data ----
covid.data$age_gr <- factor(covid.data$age_gr,
levels = c("Under 18","18 to 39", "40 to 59", "60 to 69",
"70 to 79", "80 or older"))
covid.data$gender <- factor(covid.data$gender,
levels = c("Male", "Female"))
covid.data$charlson <- factor(covid.data$charlson,
levels = c("0", "1", "2", "3+"))
# get 67 day cumualative incidence for initial transitions -----
events.t1_t3<-events %>%
filter(trans %in% c(1:3))
#cuminc
c.inc_fit<-list()
#overall
c.inc_fit[["overall"]] <- cuminc(ftime = covid.data$healthy_c.time,
fstatus = covid.data$healthy_c.event)
#by groups
groups<-c("age_gr", "gender",
"charlson",
"a_autoimmune_condition",
"a_chronic_kidney_disease", "a_copd",
"a_dementia","a_heart_disease","a_hyperlipidemia",
"a_hypertension", "a_malignant_neoplasm",
"a_obesity.5y","a_t2_diabetes")
for(i in 1:length(groups)){
message(paste0("Working on ", i, " of ", length(groups)))
c.inc_fit[[groups[i]]]<- cuminc(ftime = covid.data$healthy_c.time,
fstatus = covid.data$healthy_c.event,
group = covid.data[[groups[i]]])
}
# extract estimates at 67 days
tp<-list()
tp[["overall"]]<-timepoints(c.inc_fit[["overall"]],67)$est
for(i in 1:length(groups)){
tp[[groups[i]]]<- timepoints(c.inc_fit[[groups[i]]],67)$est
}
t1<-data.frame(
trans=1,
group=c("all", rep("Age",6),
rep("Sex",2),
rep("Charlson",4),
rep("Autoimmune condition",2),
rep("Chronic kidney disease",2),
rep("COPD",2),
rep("Dementia",2),
rep("Heart disease",2),
rep("Hyperlipidemia",2),
rep("Malignant neoplasm",2),
rep("Obesity",2),
rep("Type 2 diabetes",2)
),
level=c("all",
levels(covid.data$age_gr),
levels(covid.data$gender),
levels(covid.data$charlson),
"0", "1","0", "1","0", "1","0", "1","0", "1","0", "1",
"0", "1","0", "1","0", "1"),
est=c(
tp$overall[1],
tp$age_gr[1:6],
tp$gender[1:2],
tp$charlson[1:4],
tp$a_autoimmune_condition[1:2],
tp$a_chronic_kidney_disease[1:2],
tp$a_copd[1:2],
tp$a_dementia[1:2],
tp$a_heart_disease[1:2],
tp$a_hyperlipidemia[1:2],
tp$a_malignant_neoplasm[1:2],
tp$a_obesity.5y[1:2],
tp$a_t2_diabetes[1:2]
))
t2<-data.frame(
trans=2,
group=c("all", rep("Age",6),
rep("Sex",2),
rep("Charlson",4),
rep("Autoimmune condition",2),
rep("Chronic kidney disease",2),
rep("COPD",2),
rep("Dementia",2),
rep("Heart disease",2),
rep("Hyperlipidemia",2),
rep("Malignant neoplasm",2),
rep("Obesity",2),
rep("Type 2 diabetes",2)
),
level=c("all",
levels(covid.data$age_gr),
levels(covid.data$gender),
levels(covid.data$charlson),
"0", "1","0", "1","0", "1","0", "1","0", "1","0", "1",
"0", "1","0", "1","0", "1"),
est=c(
tp$overall[2],
tp$age_gr[7:12],
tp$gender[3:4],
tp$charlson[5:8],
tp$a_autoimmune_condition[3:4],
tp$a_chronic_kidney_disease[3:4],
tp$a_copd[3:4],
tp$a_dementia[3:4],
tp$a_heart_disease[3:4],
tp$a_hyperlipidemia[3:4],
tp$a_malignant_neoplasm[3:4],
tp$a_obesity.5y[3:4],
tp$a_t2_diabetes[3:4]
))
t3<-data.frame(
trans=3,
group=c("all", rep("Age",6),
rep("Sex",2),
rep("Charlson",4),
rep("Autoimmune condition",2),
rep("Chronic kidney disease",2),
rep("COPD",2),
rep("Dementia",2),
rep("Heart disease",2),
rep("Hyperlipidemia",2),
rep("Malignant neoplasm",2),
rep("Obesity",2),
rep("Type 2 diabetes",2)
),
level=c("all",
levels(covid.data$age_gr),
levels(covid.data$gender),
levels(covid.data$charlson),
"0", "1","0", "1","0", "1","0", "1","0", "1","0", "1",
"0", "1","0", "1","0", "1"),
est=c(
tp$overall[3],
tp$age_gr[13:18],
tp$gender[5:6],
tp$charlson[9:12],
tp$a_autoimmune_condition[5:6],
tp$a_chronic_kidney_disease[5:6],
tp$a_copd[5:6],
tp$a_dementia[5:6],
tp$a_heart_disease[5:6],
tp$a_hyperlipidemia[5:6],
tp$a_malignant_neoplasm[5:6],
tp$a_obesity.5y[5:6],
tp$a_t2_diabetes[5:6]
))
est<-rbind(t1,t2,t3)
# add to table
events.t1_t3<-events.t1_t3 %>%
left_join(est,
by = c("trans","group", "level"))
# format number
events.t1_t3<-events.t1_t3 %>%
mutate(est=paste0(nice.num2(est*100), "%"))
# pivot wide- transitions on same row
events.t1_t3.wide<-events.t1_t3 %>%
pivot_wider(names_from = trans,
values_from = c(events, est))
# formatting
events.t1_t3.wide<-events.t1_t3.wide %>%
mutate(events_diag.general.pop=paste0(events_1, " (", est_1, ")")) %>%
mutate(events_hosp.general.pop=paste0(events_2, " (", est_2, ")")) %>%
mutate(events_death.general.pop=paste0(events_3, " (", est_3, ")")) %>%
select(group, level, n, time, events_diag.general.pop, events_hosp.general.pop, events_death.general.pop) %>%
rename( n.general.pop=n,
time.general.pop=time)# get 45 day cumualative incidence for transitions from diagnosis ----
events.t4_t5<-events %>%
filter(trans %in% c(4:5))
#cuminc
c.inc_fit<-list()
quiet <- function(x) {
sink(tempfile())
on.exit(sink())
invisible(force(x))
}
# use quiet to suppress message about missing values (we only want pop for specific state, others are missing)
#overall
c.inc_fit[["overall"]] <- quiet(cuminc(ftime = covid.data$diagnosis_c.time,
fstatus = covid.data$diagnosis_c.event))
#by groups
groups<-c("age_gr", "gender",
"charlson",
"a_autoimmune_condition",
"a_chronic_kidney_disease", "a_copd",
"a_dementia","a_heart_disease","a_hyperlipidemia",
"a_hypertension", "a_malignant_neoplasm",
"a_obesity.5y","a_t2_diabetes")
for(i in 1:length(groups)){
message(paste0("Working on ", i, " of ", length(groups)))
c.inc_fit[[groups[i]]]<- quiet(cuminc(ftime = covid.data$diagnosis_c.time,
fstatus = covid.data$diagnosis_c.event,
group = covid.data[[groups[i]]]) )
}
# extract estimates at 45 days
tp<-list()
tp[["overall"]]<-timepoints(c.inc_fit[["overall"]],45)$est
for(i in 1:length(groups)){
tp[[groups[i]]]<- timepoints(c.inc_fit[[groups[i]]],45)$est
}
t1<-data.frame(
trans=4,
group=c("all", rep("Age",6),
rep("Sex",2),
rep("Charlson",4),
rep("Autoimmune condition",2),
rep("Chronic kidney disease",2),
rep("COPD",2),
rep("Dementia",2),
rep("Heart disease",2),
rep("Hyperlipidemia",2),
rep("Malignant neoplasm",2),
rep("Obesity",2),
rep("Type 2 diabetes",2)
),
level=c("all",
levels(covid.data$age_gr),
levels(covid.data$gender),
levels(covid.data$charlson),
"0", "1","0", "1","0", "1","0", "1","0", "1","0", "1",
"0", "1","0", "1","0", "1"),
est=c(
tp$overall[1],
tp$age_gr[1:6],
tp$gender[1:2],
tp$charlson[1:4],
tp$a_autoimmune_condition[1:2],
tp$a_chronic_kidney_disease[1:2],
tp$a_copd[1:2],
tp$a_dementia[1:2],
tp$a_heart_disease[1:2],
tp$a_hyperlipidemia[1:2],
tp$a_malignant_neoplasm[1:2],
tp$a_obesity.5y[1:2],
tp$a_t2_diabetes[1:2]
))
t2<-data.frame(
trans=5,
group=c("all", rep("Age",6),
rep("Sex",2),
rep("Charlson",4),
rep("Autoimmune condition",2),
rep("Chronic kidney disease",2),
rep("COPD",2),
rep("Dementia",2),
rep("Heart disease",2),
rep("Hyperlipidemia",2),
rep("Malignant neoplasm",2),
rep("Obesity",2),
rep("Type 2 diabetes",2)
),
level=c("all",
levels(covid.data$age_gr),
levels(covid.data$gender),
levels(covid.data$charlson),
"0", "1","0", "1","0", "1","0", "1","0", "1","0", "1",
"0", "1","0", "1","0", "1"),
est=c(
tp$overall[2],
tp$age_gr[7:12],
tp$gender[3:4],
tp$charlson[5:8],
tp$a_autoimmune_condition[3:4],
tp$a_chronic_kidney_disease[3:4],
tp$a_copd[3:4],
tp$a_dementia[3:4],
tp$a_heart_disease[3:4],
tp$a_hyperlipidemia[3:4],
tp$a_malignant_neoplasm[3:4],
tp$a_obesity.5y[3:4],
tp$a_t2_diabetes[3:4]
))
est<-rbind(t1,t2)
##
# add to table
events.t4_t5<-events.t4_t5 %>%
left_join(est,
by = c("trans","group", "level"))
# format numbers
events.t4_t5<-events.t4_t5 %>%
mutate(est=paste0(nice.num2(est*100), "%"))
# pivot wide
events.t4_t5.wide<-events.t4_t5 %>%
pivot_wider(names_from = trans,
values_from = c(events, est))
events.t4_t5.wide<-events.t4_t5.wide %>%
mutate(events_hosp.diag=paste0(events_4, " (", est_4, ")")) %>%
mutate(events_death.diag=paste0(events_5, " (", est_5, ")")) %>%
select(group, level, n, time, events_hosp.diag, events_death.diag) %>%
rename(n.diag=n,
time.diagn=time)# get 45 days cumualative incidence for transition from hospitalised ----
events.t6<-events %>%
filter(trans %in% c(6))
#cuminc
c.inc_fit<-list()
quiet <- function(x) {
sink(tempfile())
on.exit(sink())
invisible(force(x))
}
# use quiet to suppress message about missing values (we only want pop for specific state, others are missing)
#overall
c.inc_fit[["overall"]] <- quiet(cuminc(ftime = r.hospitalised.death$time,
fstatus = r.hospitalised.death$status))
#by groups
groups<-c("age_gr", "gender",
"charlson",
"a_autoimmune_condition",
"a_chronic_kidney_disease", "a_copd",
"a_dementia","a_heart_disease","a_hyperlipidemia",
"a_hypertension", "a_malignant_neoplasm",
"a_obesity.5y","a_t2_diabetes")
for(i in 1:length(groups)){
message(paste0("Working on ", i, " of ", length(groups)))
c.inc_fit[[groups[i]]]<- quiet(cuminc(ftime = r.hospitalised.death$time,
fstatus = r.hospitalised.death$status,
group = r.hospitalised.death[[groups[i]]]) )
}
# extract estimates at 45 days
tp<-list()
tp[["overall"]]<-timepoints(c.inc_fit[["overall"]],45)$est
for(i in 1:length(groups)){
tp[[groups[i]]]<- timepoints(c.inc_fit[[groups[i]]],45)$est
}
t1<-data.frame(
trans=6,
group=c("all", rep("Age",6),
rep("Sex",2),
rep("Charlson",4),
rep("Autoimmune condition",2),
rep("Chronic kidney disease",2),
rep("COPD",2),
rep("Dementia",2),
rep("Heart disease",2),
rep("Hyperlipidemia",2),
rep("Malignant neoplasm",2),
rep("Obesity",2),
rep("Type 2 diabetes",2)
),
level=c("all",
levels(covid.data$age_gr),
levels(covid.data$gender),
levels(covid.data$charlson),
"0", "1","0", "1","0", "1","0", "1","0", "1","0", "1",
"0", "1","0", "1","0", "1"),
est=c(
tp$overall[1],
tp$age_gr[1:6],
tp$gender[1:2],
tp$charlson[1:4],
tp$a_autoimmune_condition[1:2],
tp$a_chronic_kidney_disease[1:2],
tp$a_copd[1:2],
tp$a_dementia[1:2],
tp$a_heart_disease[1:2],
tp$a_hyperlipidemia[1:2],
tp$a_malignant_neoplasm[1:2],
tp$a_obesity.5y[1:2],
tp$a_t2_diabetes[1:2]
))
# add to table
events.t6<-events.t6 %>%
left_join(t1,
by = c("trans","group", "level"))
# format number
events.t6<-events.t6 %>%
mutate(est=paste0(nice.num2(est*100), "%"))
# pivot wide for consistency in names etc
events.t6.wide<-events.t6 %>%
pivot_wider(names_from = trans,
values_from = c(events, est))
events.t6.wide<-events.t6.wide %>%
mutate(events_death.hosp=paste0(events_6, " (", est_6, ")")) %>%
select(group, level, n, time, events_death.hosp) %>%
rename(n.hosp=n,
time.hosp=time)a<-events.t1_t3.wide %>%
left_join(events.t4_t5.wide) %>%
left_join(events.t6.wide)
# obscure any counts less than 5
b<-apply(a, 2,
function(x)
ifelse(str_sub(x, 1, 2) %in% c("0 ", "1 ","2 ", "3 ","4 "),
"<5",x))
kable(a,
col.names = c("", "",
"n",
"Median (min, interquartile range, max)",
"Events (cumulative incidence at 67 days)",
"Events (cumulative incidence at 67 days)",
"Events (cumulative incidence at 67 days",
"n",
"Median (min, interquartile range, max)",
"Events (cumulative incidence at 45 days)",
"Events (cumulative incidence at 45 days)",
"n",
"Median (min, interquartile range, max)",
"Events (cumulative incidence at 45 days)")) %>%
add_header_above(c(" " = 3,
"Follow-up in days",
"To diagnosis with COVID-19",
"To hospitalised with COVID-19",
"To death",
" ",
"Follow-up in days",
"To hospitalised with COVID-19",
"To death",
" ",
"Follow-up in days",
"To death"))%>%
add_header_above(c(" " = 2, "From general population" = 5,
"From diagnosed with COVID-19" = 4,
"From hospitalised with COVID-19" = 3)) %>%
kable_styling(bootstrap_options = c("striped", "bordered")) | n | Median (min, interquartile range, max) | Events (cumulative incidence at 67 days) | Events (cumulative incidence at 67 days) | Events (cumulative incidence at 67 days | n | Median (min, interquartile range, max) | Events (cumulative incidence at 45 days) | Events (cumulative incidence at 45 days) | n | Median (min, interquartile range, max) | Events (cumulative incidence at 45 days) | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| all | all | 5,627,520 | 67 (1, 67 to 67, 67) | 109,367 (1.94%) | 8,582 (0.15%) | 11,726 (0.21%) | 109,367 | 34 (0, 18 to 44, 66) | 9,437 (9.03%) | 2,794 (3.11%) | 18,019 | 36 (0, 23 to 42, 65) | 2,791 (19.03%) |
| Age | Under 18 | 967,227 | 67 (1, 67 to 67, 67) | 4,547 (0.47%) | 34 (0.0035%) | 9 (0.00093%) | 4,547 | 29 (0, 16 to 40, 65) | 40 (0.96%) | 0 (0.00%) | 74 | 28 (0, 19 to 41.75, 55) | 1 (2.13%) |
| Age | 18 to 39 | 1,438,732 | 67 (1, 67 to 67, 67) | 30,640 (2.13%) | 432 (0.03%) | 80 (0.0056%) | 30,640 | 37 (0, 22 to 44, 66) | 857 (2.92%) | 5 (0.031%) | 1,289 | 36 (0, 27 to 43, 65) | 11 (1.14%) |
| Age | 40 to 59 | 1,788,832 | 67 (1, 67 to 67, 67) | 44,803 (2.51%) | 1,815 (0.10%) | 658 (0.037%) | 44,803 | 37 (0, 22 to 44, 66) | 3,477 (8.08%) | 63 (0.18%) | 5,292 | 37 (0, 29 to 43, 65) | 143 (3.52%) |
| Age | 60 to 69 | 617,929 | 67 (1, 67 to 67, 67) | 10,661 (1.73%) | 1,590 (0.26%) | 1,034 (0.17%) | 10,661 | 34 (0, 14 to 43, 65) | 1,708 (16.62%) | 136 (1.53%) | 3,298 | 38 (0, 30 to 44, 64) | 296 (10.78%) |
| Age | 70 to 79 | 474,369 | 67 (1, 67 to 67, 67) | 7,163 (1.51%) | 2,252 (0.47%) | 1,999 (0.42%) | 7,163 | 22 (0, 7 to 39, 65) | 1,702 (24.79%) | 421 (7.54%) | 3,954 | 37 (0, 24 to 43, 65) | 840 (24.71%) |
| Age | 80 or older | 340,431 | 67 (1, 67 to 67, 67) | 11,553 (3.40%) | 2,459 (0.72%) | 7,946 (2.34%) | 11,553 | 19 (0, 9 to 29, 62) | 1,653 (15.49%) | 2,169 (29.55%) | 4,112 | 25 (0, 14 to 37, 65) | 1,500 (47.86%) |
| Sex | Male | 2,768,246 | 67 (1, 67 to 67, 67) | 44,894 (1.62%) | 4,812 (0.17%) | 5,794 (0.21%) | 44,894 | 34 (0, 16 to 44, 66) | 5,080 (11.81%) | 1,169 (3.19%) | 9,892 | 36 (0, 25 to 43, 65) | 1,669 (20.47%) |
| Sex | Female | 2,859,274 | 67 (1, 67 to 67, 67) | 64,473 (2.26%) | 3,770 (0.13%) | 5,932 (0.21%) | 64,473 | 34 (0, 19 to 44, 66) | 4,357 (7.09%) | 1,625 (3.05%) | 8,127 | 35 (0, 23 to 42, 65) | 1,122 (17.15%) |
| Charlson | 0 | 4,572,265 | 67 (1, 67 to 67, 67) | 81,833 (1.79%) | 3,750 (0.082%) | 1,672 (0.037%) | 81,833 | 36 (0, 21 to 44, 66) | 5,513 (7.03%) | 391 (0.58%) | 9,263 | 37 (0, 28 to 43, 65) | 604 (8.33%) |
| Charlson | 1 | 410,497 | 67 (1, 67 to 67, 67) | 10,708 (2.61%) | 1,156 (0.28%) | 1,724 (0.42%) | 10,708 | 29 (0, 15 to 42, 65) | 1,157 (11.33%) | 566 (6.60%) | 2,313 | 35 (0, 21 to 43, 65) | 447 (23.59%) |
| Charlson | 2 | 363,057 | 67 (2, 67 to 67, 67) | 8,211 (2.26%) | 1,518 (0.42%) | 2,398 (0.66%) | 8,211 | 27 (0, 12 to 41, 65) | 1,266 (16.16%) | 507 (7.95%) | 2,784 | 35 (0, 22 to 42, 65) | 572 (24.67%) |
| Charlson | 3+ | 281,701 | 67 (1, 67 to 67, 67) | 8,615 (3.06%) | 2,158 (0.77%) | 5,932 (2.11%) | 8,615 | 20 (0, 9 to 33, 64) | 1,501 (18.56%) | 1,330 (21.49%) | 3,659 | 29 (0, 16 to 40, 65) | 1,168 (40.08%) |
| Autoimmune condition | 0 | 5,350,070 | 67 (1, 67 to 67, 67) | 102,431 (1.92%) | 7,711 (0.14%) | 10,406 (0.19%) | 102,431 | 34 (0, 18 to 44, 66) | 8,651 (8.84%) | 2,503 (2.98%) | 16,362 | 36 (0, 24 to 43, 65) | 2,451 (18.45%) |
| Autoimmune condition | 1 | 277,450 | 67 (2, 67 to 67, 67) | 6,936 (2.50%) | 871 (0.31%) | 1,320 (0.48%) | 6,936 | 32 (0, 15 to 43, 64) | 786 (11.82%) | 291 (4.98%) | 1,657 | 34 (0, 21 to 42, 65) | 340 (24.74%) |
| Chronic kidney disease | 0 | 5,415,678 | 67 (1, 67 to 67, 67) | 103,524 (1.91%) | 7,034 (0.13%) | 7,882 (0.15%) | 103,524 | 35 (0, 19 to 44, 66) | 8,379 (8.46%) | 1,839 (2.17%) | 15,413 | 36 (0, 25 to 43, 65) | 1,933 (15.44%) |
| Chronic kidney disease | 1 | 211,842 | 67 (1, 67 to 67, 67) | 5,843 (2.76%) | 1,548 (0.73%) | 3,844 (1.82%) | 5,843 | 19 (0, 9 to 33, 62) | 1,058 (19.26%) | 955 (22.31%) | 2,606 | 29 (0, 16 to 40, 64) | 858 (41.16%) |
| COPD | 0 | 5,504,775 | 67 (1, 67 to 67, 67) | 106,478 (1.94%) | 7,781 (0.14%) | 10,238 (0.19%) | 106,478 | 34 (0, 19 to 44, 66) | 8,922 (8.76%) | 2,514 (2.87%) | 16,703 | 36 (0, 24 to 43, 65) | 2,412 (17.75%) |
| COPD | 1 | 122,745 | 67 (2, 67 to 67, 67) | 2,889 (2.35%) | 801 (0.65%) | 1,488 (1.21%) | 2,889 | 22 (0, 9 to 37, 65) | 515 (18.85%) | 280 (12.75%) | 1,316 | 31 (0, 18 to 42, 65) | 379 (35.42%) |
| Dementia | 0 | 5,570,210 | 67 (1, 67 to 67, 67) | 104,572 (1.88%) | 7,984 (0.14%) | 8,832 (0.16%) | 104,572 | 35 (0, 19 to 44, 66) | 8,951 (8.94%) | 1,686 (1.95%) | 16,935 | 36 (0, 25 to 43, 65) | 2,375 (17.24%) |
| Dementia | 1 | 57,310 | 67 (1, 67 to 67, 67) | 4,795 (8.37%) | 598 (1.04%) | 2,894 (5.05%) | 4,795 | 19 (0, 11 to 27, 59) | 486 (11.11%) | 1,108 (40.04%) | 1,084 | 21 (0, 12 to 31, 65) | 416 (54.96%) |
| Heart disease | 0 | 5,079,725 | 67 (1, 67 to 67, 67) | 95,827 (1.89%) | 5,736 (0.11%) | 5,861 (0.12%) | 95,827 | 35 (0, 19 to 44, 66) | 7,305 (7.97%) | 1,530 (1.93%) | 13,041 | 36 (0, 26 to 43, 65) | 1,420 (13.67%) |
| Heart disease | 1 | 547,795 | 67 (1, 67 to 67, 67) | 13,540 (2.47%) | 2,846 (0.52%) | 5,865 (1.07%) | 13,540 | 23 (0, 11 to 40, 65) | 2,132 (16.55%) | 1,264 (12.07%) | 4,978 | 32 (0, 18 to 41, 65) | 1,371 (33.29%) |
| Hyperlipidemia | 0 | 5,106,683 | 67 (1, 67 to 67, 67) | 97,479 (1.91%) | 6,992 (0.14%) | 10,096 (0.20%) | 97,479 | 34 (0, 19 to 44, 66) | 7,838 (8.41%) | 2,379 (2.97%) | 14,830 | 35 (0, 23 to 42, 65) | 2,268 (18.75%) |
| Hyperlipidemia | 1 | 520,837 | 67 (1, 67 to 67, 67) | 11,888 (2.28%) | 1,590 (0.31%) | 1,630 (0.31%) | 11,888 | 30 (0, 14 to 43, 65) | 1,599 (14.04%) | 415 (4.27%) | 3,189 | 36 (0, 25 to 43, 65) | 523 (20.21%) |
| Malignant neoplasm | 0 | 5,330,111 | 67 (1, 67 to 67, 67) | 102,711 (1.93%) | 7,177 (0.13%) | 7,908 (0.15%) | 102,711 | 34 (0, 19 to 44, 66) | 8,362 (8.52%) | 2,168 (2.55%) | 15,539 | 36 (0, 24 to 43, 65) | 2,100 (16.62%) |
| Malignant neoplasm | 1 | 297,409 | 67 (1, 67 to 67, 67) | 6,656 (2.24%) | 1,405 (0.47%) | 3,818 (1.28%) | 6,656 | 26.5 (0, 11 to 41, 65) | 1,075 (16.86%) | 626 (12.00%) | 2,480 | 33 (0, 19 to 42, 65) | 691 (33.72%) |
| Obesity | 0 | 4,689,927 | 67 (1, 67 to 67, 67) | 86,874 (1.85%) | 5,198 (0.11%) | 8,475 (0.18%) | 86,874 | 35 (0, 19 to 44, 66) | 6,151 (7.42%) | 2,061 (2.90%) | 11,349 | 35 (0, 23 to 42, 65) | 1,673 (18.51%) |
| Obesity | 1 | 937,593 | 67 (1, 67 to 67, 67) | 22,493 (2.40%) | 3,384 (0.36%) | 3,251 (0.35%) | 22,493 | 30 (0, 14 to 42, 66) | 3,286 (15.22%) | 733 (3.92%) | 6,670 | 36 (0, 24 to 43, 65) | 1,118 (19.88%) |
| Type 2 diabetes | 0 | 5,302,576 | 67 (1, 67 to 67, 67) | 101,940 (1.92%) | 6,809 (0.13%) | 9,042 (0.17%) | 101,940 | 35 (0, 19 to 44, 66) | 8,015 (8.22%) | 2,157 (2.58%) | 14,824 | 36 (0, 24 to 43, 65) | 2,057 (17.15%) |
| Type 2 diabetes | 1 | 324,944 | 67 (2, 67 to 67, 67) | 7,427 (2.29%) | 1,773 (0.55%) | 2,684 (0.83%) | 7,427 | 22 (0, 9 to 38, 64) | 1,422 (20.10%) | 637 (10.93%) | 3,195 | 34 (0, 21 to 42, 65) | 734 (27.80%) |
Transition 1: From general population to diagnosed with COVID-19
overall
survfit.t1<-survfit(Surv(time, status) ~ 1,
data = r.healthy.diagnosis)
ggsurvplot.event<-ggsurvplot(survfit.t1, fun = "event", palette="black", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.position = "none")
ggsave( "ggsurv.t1.png",print(ggsurvplot.event),
dpi=300,
width = 6, height = 5)
include_graphics("ggsurv.t1.png")
survfit.t1<-survfit(Surv(time, status) ~ 1,
data = r.healthy.diagnosis)
ggsurvplot.event<-ggsurvplot(survfit.t1, fun = "event", conf.int =TRUE)
at.risk.t1<-ggsurvplot(survfit.t1, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
mutate(date=as.Date("29/02/2020",
"%d/%m/%y")+time) %>%
select(date, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t1,
col.names = c("Date", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Date | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|
| 2020-02-29 | 5,627,520 | 0 | 0 |
| 2020-03-05 | 5,626,131 | 184 | 1,522 |
| 2020-03-10 | 5,624,299 | 976 | 2,788 |
| 2020-03-15 | 5,618,990 | 4,372 | 4,855 |
| 2020-03-20 | 5,601,568 | 23,202 | 7,389 |
| 2020-03-25 | 5,581,168 | 41,194 | 10,065 |
| 2020-03-30 | 5,564,967 | 55,519 | 12,467 |
| 2020-04-04 | 5,545,976 | 67,934 | 15,044 |
| 2020-04-09 | 5,534,483 | 78,825 | 17,145 |
| 2020-04-14 | 5,526,416 | 86,066 | 18,149 |
| 2020-04-19 | 5,514,426 | 93,399 | 20,393 |
| 2020-04-24 | 5,505,711 | 100,897 | 22,527 |
| 2020-04-29 | 5,500,060 | 105,153 | 24,223 |
| 2020-05-04 | 5,495,815 | 107,765 | 25,280 |
By gender
survfit.t1.gender<-survfit(Surv(time, status) ~ gender,
data = r.healthy.diagnosis)
ggsurvplot.event<-ggsurvplot(survfit.t1.gender, fun = "event", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.title = element_blank())
ggsurvplot.cloglog<-ggsurvplot(survfit.t1.gender, fun = "cloglog", conf.int =TRUE)
ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.position = "none")
ggsurv.t1.gender<- list(
ggsurvplot.event,
ggsurvplot.cloglog)
ggsurv.t1.gender <-arrange_ggsurvplots(ggsurv.t1.gender, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t1.gender.png",ggsurv.t1.gender,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t1.gender.png")
at.risk.t1.gender<-ggsurvplot(survfit.t1.gender, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
mutate(date=as.Date("29/02/2020",
"%d/%m/%y")+time) %>%
select(date, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t1.gender,
col.names = c("Date", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Date | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 2020-02-29 | gender=Male | 2,768,246 | 0 | 0 |
| 2020-02-29 | gender=Female | 2,859,274 | 0 | 0 |
| 2020-03-05 | gender=Male | 2,767,555 | 62 | 796 |
| 2020-03-05 | gender=Female | 2,858,576 | 122 | 726 |
| 2020-03-10 | gender=Male | 2,766,634 | 402 | 1,467 |
| 2020-03-10 | gender=Female | 2,857,665 | 574 | 1,321 |
| 2020-03-15 | gender=Male | 2,764,197 | 1,826 | 2,542 |
| 2020-03-15 | gender=Female | 2,854,793 | 2,546 | 2,313 |
| 2020-03-20 | gender=Male | 2,756,157 | 10,237 | 3,944 |
| 2020-03-20 | gender=Female | 2,845,411 | 12,965 | 3,445 |
| 2020-03-25 | gender=Male | 2,746,832 | 18,211 | 5,464 |
| 2020-03-25 | gender=Female | 2,834,336 | 22,983 | 4,601 |
| 2020-03-30 | gender=Male | 2,739,460 | 24,305 | 6,833 |
| 2020-03-30 | gender=Female | 2,825,507 | 31,214 | 5,634 |
| 2020-04-04 | gender=Male | 2,731,513 | 29,111 | 8,214 |
| 2020-04-04 | gender=Female | 2,814,463 | 38,823 | 6,830 |
| 2020-04-09 | gender=Male | 2,726,839 | 33,314 | 9,298 |
| 2020-04-09 | gender=Female | 2,807,644 | 45,511 | 7,847 |
| 2020-04-14 | gender=Male | 2,723,689 | 36,056 | 9,766 |
| 2020-04-14 | gender=Female | 2,802,727 | 50,010 | 8,383 |
| 2020-04-19 | gender=Male | 2,718,947 | 38,699 | 10,856 |
| 2020-04-19 | gender=Female | 2,795,479 | 54,700 | 9,537 |
| 2020-04-24 | gender=Male | 2,715,537 | 41,528 | 11,886 |
| 2020-04-24 | gender=Female | 2,790,174 | 59,369 | 10,641 |
| 2020-04-29 | gender=Male | 2,713,248 | 43,138 | 12,704 |
| 2020-04-29 | gender=Female | 2,786,812 | 62,015 | 11,519 |
| 2020-05-04 | gender=Male | 2,711,351 | 44,268 | 13,247 |
| 2020-05-04 | gender=Female | 2,784,464 | 63,497 | 12,033 |
By age
survfit.t1.age_gr<-survfit(Surv(time, status) ~ age_gr,
data = r.healthy.diagnosis)
ggsurv.t1.age_gr<- list(
ggsurvplot(survfit.t1.age_gr, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t1.age_gr, fun = "cloglog", conf.int =TRUE))
ggsurv.t1.age_gr <-arrange_ggsurvplots(ggsurv.t1.age_gr, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t1.age_gr.png",ggsurv.t1.age_gr,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t1.age_gr.png")
at.risk.t1.age_gr<-ggsurvplot(survfit.t1.age_gr, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
mutate(date=as.Date("29/02/2020",
"%d/%m/%y")+time) %>%
select(date, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t1.age_gr,
col.names = c("Date", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Date | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 2020-02-29 | age_gr=Under 18 | 967,227 | 0 | 0 |
| 2020-02-29 | age_gr=18 to 39 | 1,438,732 | 0 | 0 |
| 2020-02-29 | age_gr=40 to 59 | 1,788,832 | 0 | 0 |
| 2020-02-29 | age_gr=60 to 69 | 617,929 | 0 | 0 |
| 2020-02-29 | age_gr=70 to 79 | 474,369 | 0 | 0 |
| 2020-02-29 | age_gr=80 or older | 340,431 | 0 | 0 |
| 2020-03-05 | age_gr=Under 18 | 967,148 | 3 | 96 |
| 2020-03-05 | age_gr=18 to 39 | 1,438,396 | 51 | 396 |
| 2020-03-05 | age_gr=40 to 59 | 1,788,516 | 89 | 295 |
| 2020-03-05 | age_gr=60 to 69 | 617,797 | 30 | 140 |
| 2020-03-05 | age_gr=70 to 79 | 474,235 | 7 | 151 |
| 2020-03-05 | age_gr=80 or older | 340,039 | 4 | 444 |
| 2020-03-10 | age_gr=Under 18 | 967,057 | 13 | 170 |
| 2020-03-10 | age_gr=18 to 39 | 1,437,927 | 235 | 724 |
| 2020-03-10 | age_gr=40 to 59 | 1,788,016 | 468 | 556 |
| 2020-03-10 | age_gr=60 to 69 | 617,593 | 139 | 259 |
| 2020-03-10 | age_gr=70 to 79 | 474,071 | 63 | 279 |
| 2020-03-10 | age_gr=80 or older | 339,635 | 58 | 800 |
| 2020-03-15 | age_gr=Under 18 | 966,925 | 41 | 273 |
| 2020-03-15 | age_gr=18 to 39 | 1,436,495 | 1,265 | 1,135 |
| 2020-03-15 | age_gr=40 to 59 | 1,786,021 | 2,131 | 964 |
| 2020-03-15 | age_gr=60 to 69 | 617,051 | 487 | 476 |
| 2020-03-15 | age_gr=70 to 79 | 473,621 | 263 | 581 |
| 2020-03-15 | age_gr=80 or older | 338,877 | 185 | 1,426 |
| 2020-03-20 | age_gr=Under 18 | 966,550 | 510 | 299 |
| 2020-03-20 | age_gr=18 to 39 | 1,431,489 | 7,071 | 1,402 |
| 2020-03-20 | age_gr=40 to 59 | 1,778,104 | 11,222 | 1,404 |
| 2020-03-20 | age_gr=60 to 69 | 615,231 | 2,424 | 850 |
| 2020-03-20 | age_gr=70 to 79 | 472,402 | 1,217 | 1,159 |
| 2020-03-20 | age_gr=80 or older | 337,792 | 758 | 2,275 |
| 2020-03-25 | age_gr=Under 18 | 966,050 | 993 | 332 |
| 2020-03-25 | age_gr=18 to 39 | 1,426,062 | 12,307 | 1,609 |
| 2020-03-25 | age_gr=40 to 59 | 1,769,519 | 19,296 | 1,907 |
| 2020-03-25 | age_gr=60 to 69 | 612,789 | 4,398 | 1,317 |
| 2020-03-25 | age_gr=70 to 79 | 470,557 | 2,479 | 1,803 |
| 2020-03-25 | age_gr=80 or older | 336,191 | 1,721 | 3,097 |
| 2020-03-30 | age_gr=Under 18 | 965,564 | 1,457 | 352 |
| 2020-03-30 | age_gr=18 to 39 | 1,422,131 | 16,227 | 1,794 |
| 2020-03-30 | age_gr=40 to 59 | 1,763,306 | 25,497 | 2,376 |
| 2020-03-30 | age_gr=60 to 69 | 610,854 | 5,950 | 1,714 |
| 2020-03-30 | age_gr=70 to 79 | 468,883 | 3,469 | 2,383 |
| 2020-03-30 | age_gr=80 or older | 334,229 | 2,919 | 3,848 |
| 2020-04-04 | age_gr=Under 18 | 964,820 | 2,048 | 377 |
| 2020-04-04 | age_gr=18 to 39 | 1,417,358 | 19,687 | 1,970 |
| 2020-04-04 | age_gr=40 to 59 | 1,756,123 | 30,302 | 2,802 |
| 2020-04-04 | age_gr=60 to 69 | 608,859 | 7,167 | 2,049 |
| 2020-04-04 | age_gr=70 to 79 | 467,358 | 4,334 | 2,857 |
| 2020-04-04 | age_gr=80 or older | 331,458 | 4,396 | 4,989 |
| 2020-04-09 | age_gr=Under 18 | 964,348 | 2,634 | 398 |
| 2020-04-09 | age_gr=18 to 39 | 1,414,606 | 22,613 | 2,142 |
| 2020-04-09 | age_gr=40 to 59 | 1,752,126 | 34,418 | 3,157 |
| 2020-04-09 | age_gr=60 to 69 | 607,762 | 8,154 | 2,289 |
| 2020-04-09 | age_gr=70 to 79 | 466,393 | 5,023 | 3,211 |
| 2020-04-09 | age_gr=80 or older | 329,248 | 5,983 | 5,948 |
| 2020-04-14 | age_gr=Under 18 | 963,998 | 2,977 | 410 |
| 2020-04-14 | age_gr=18 to 39 | 1,412,804 | 24,489 | 2,211 |
| 2020-04-14 | age_gr=40 to 59 | 1,749,659 | 36,961 | 3,325 |
| 2020-04-14 | age_gr=60 to 69 | 607,070 | 8,739 | 2,392 |
| 2020-04-14 | age_gr=70 to 79 | 465,724 | 5,495 | 3,378 |
| 2020-04-14 | age_gr=80 or older | 327,161 | 7,405 | 6,433 |
| 2020-04-19 | age_gr=Under 18 | 963,456 | 3,338 | 459 |
| 2020-04-19 | age_gr=18 to 39 | 1,410,279 | 26,193 | 2,388 |
| 2020-04-19 | age_gr=40 to 59 | 1,746,184 | 39,227 | 3,582 |
| 2020-04-19 | age_gr=60 to 69 | 606,044 | 9,358 | 2,578 |
| 2020-04-19 | age_gr=70 to 79 | 464,575 | 6,119 | 3,740 |
| 2020-04-19 | age_gr=80 or older | 323,888 | 9,164 | 7,646 |
| 2020-04-24 | age_gr=Under 18 | 963,003 | 3,811 | 494 |
| 2020-04-24 | age_gr=18 to 39 | 1,408,187 | 28,282 | 2,635 |
| 2020-04-24 | age_gr=40 to 59 | 1,743,638 | 41,798 | 3,877 |
| 2020-04-24 | age_gr=60 to 69 | 605,380 | 9,959 | 2,759 |
| 2020-04-24 | age_gr=70 to 79 | 463,869 | 6,623 | 4,026 |
| 2020-04-24 | age_gr=80 or older | 321,634 | 10,424 | 8,736 |
| 2020-04-29 | age_gr=Under 18 | 962,652 | 4,141 | 531 |
| 2020-04-29 | age_gr=18 to 39 | 1,406,826 | 29,441 | 2,817 |
| 2020-04-29 | age_gr=40 to 59 | 1,741,926 | 43,290 | 4,106 |
| 2020-04-29 | age_gr=60 to 69 | 604,888 | 10,296 | 2,919 |
| 2020-04-29 | age_gr=70 to 79 | 463,397 | 6,879 | 4,284 |
| 2020-04-29 | age_gr=80 or older | 320,371 | 11,106 | 9,566 |
| 2020-05-04 | age_gr=Under 18 | 962,391 | 4,374 | 565 |
| 2020-05-04 | age_gr=18 to 39 | 1,405,894 | 30,197 | 2,986 |
| 2020-05-04 | age_gr=40 to 59 | 1,740,729 | 44,253 | 4,299 |
| 2020-05-04 | age_gr=60 to 69 | 604,521 | 10,527 | 3,002 |
| 2020-05-04 | age_gr=70 to 79 | 463,006 | 7,035 | 4,421 |
| 2020-05-04 | age_gr=80 or older | 319,274 | 11,379 | 10,007 |
By charlson
survfit.t1.charlson<-survfit(Surv(time, status) ~ charlson,
data = r.healthy.diagnosis)
ggsurv.t1.charlson<- list(
ggsurvplot(survfit.t1.charlson, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t1.charlson, fun = "cloglog", conf.int =TRUE))
ggsurv.t1.charlson <-arrange_ggsurvplots(ggsurv.t1.charlson, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t1.charlson.png",ggsurv.t1.charlson,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t1.charlson.png")
at.risk.t1.charlson<-ggsurvplot(survfit.t1.charlson, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
mutate(date=as.Date("29/02/2020",
"%d/%m/%y")+time) %>%
select(date, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t1.charlson,
col.names = c("Date", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Date | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 2020-02-29 | charlson=0 | 4,572,265 | 0 | 0 |
| 2020-02-29 | charlson=1 | 410,497 | 0 | 0 |
| 2020-02-29 | charlson=2 | 363,057 | 0 | 0 |
| 2020-02-29 | charlson=3+ | 281,701 | 0 | 0 |
| 2020-03-05 | charlson=0 | 4,571,509 | 152 | 814 |
| 2020-03-05 | charlson=1 | 410,364 | 13 | 149 |
| 2020-03-05 | charlson=2 | 362,897 | 15 | 174 |
| 2020-03-05 | charlson=3+ | 281,361 | 4 | 385 |
| 2020-03-10 | charlson=0 | 4,570,392 | 735 | 1,526 |
| 2020-03-10 | charlson=1 | 410,196 | 102 | 251 |
| 2020-03-10 | charlson=2 | 362,693 | 74 | 328 |
| 2020-03-10 | charlson=3+ | 281,018 | 65 | 683 |
| 2020-03-15 | charlson=0 | 4,566,799 | 3,432 | 2,525 |
| 2020-03-15 | charlson=1 | 409,735 | 383 | 460 |
| 2020-03-15 | charlson=2 | 362,184 | 339 | 590 |
| 2020-03-15 | charlson=3+ | 280,272 | 218 | 1,280 |
| 2020-03-20 | charlson=0 | 4,553,390 | 18,712 | 3,615 |
| 2020-03-20 | charlson=1 | 408,137 | 2,029 | 741 |
| 2020-03-20 | charlson=2 | 360,932 | 1,508 | 988 |
| 2020-03-20 | charlson=3+ | 279,109 | 953 | 2,045 |
| 2020-03-25 | charlson=0 | 4,538,019 | 32,956 | 4,784 |
| 2020-03-25 | charlson=1 | 406,294 | 3,599 | 1,089 |
| 2020-03-25 | charlson=2 | 359,276 | 2,739 | 1,449 |
| 2020-03-25 | charlson=3+ | 277,579 | 1,900 | 2,743 |
| 2020-03-30 | charlson=0 | 4,526,633 | 43,860 | 5,811 |
| 2020-03-30 | charlson=1 | 404,704 | 4,901 | 1,380 |
| 2020-03-30 | charlson=2 | 357,781 | 3,825 | 1,868 |
| 2020-03-30 | charlson=3+ | 275,849 | 2,933 | 3,408 |
| 2020-04-04 | charlson=0 | 4,513,349 | 52,984 | 6,732 |
| 2020-04-04 | charlson=1 | 402,809 | 6,130 | 1,713 |
| 2020-04-04 | charlson=2 | 356,155 | 4,772 | 2,300 |
| 2020-04-04 | charlson=3+ | 273,663 | 4,048 | 4,299 |
| 2020-04-09 | charlson=0 | 4,505,766 | 60,824 | 7,468 |
| 2020-04-09 | charlson=1 | 401,582 | 7,237 | 2,008 |
| 2020-04-09 | charlson=2 | 355,075 | 5,635 | 2,675 |
| 2020-04-09 | charlson=3+ | 272,060 | 5,129 | 4,994 |
| 2020-04-14 | charlson=0 | 4,500,816 | 65,755 | 7,796 |
| 2020-04-14 | charlson=1 | 400,626 | 8,074 | 2,146 |
| 2020-04-14 | charlson=2 | 354,220 | 6,251 | 2,856 |
| 2020-04-14 | charlson=3+ | 270,754 | 5,986 | 5,351 |
| 2020-04-19 | charlson=0 | 4,493,753 | 70,396 | 8,483 |
| 2020-04-19 | charlson=1 | 399,158 | 8,990 | 2,437 |
| 2020-04-19 | charlson=2 | 352,951 | 6,961 | 3,235 |
| 2020-04-19 | charlson=3+ | 268,564 | 7,052 | 6,238 |
| 2020-04-24 | charlson=0 | 4,488,361 | 75,669 | 9,228 |
| 2020-04-24 | charlson=1 | 398,135 | 9,812 | 2,724 |
| 2020-04-24 | charlson=2 | 352,068 | 7,564 | 3,596 |
| 2020-04-24 | charlson=3+ | 267,147 | 7,852 | 6,979 |
| 2020-04-29 | charlson=0 | 4,484,759 | 78,705 | 9,805 |
| 2020-04-29 | charlson=1 | 397,529 | 10,263 | 2,952 |
| 2020-04-29 | charlson=2 | 351,520 | 7,904 | 3,880 |
| 2020-04-29 | charlson=3+ | 266,252 | 8,281 | 7,586 |
| 2020-05-04 | charlson=0 | 4,482,261 | 80,660 | 10,272 |
| 2020-05-04 | charlson=1 | 397,044 | 10,534 | 3,052 |
| 2020-05-04 | charlson=2 | 351,026 | 8,096 | 4,049 |
| 2020-05-04 | charlson=3+ | 265,484 | 8,475 | 7,907 |
By a_autoimmune_condition
survfit.t1.a_autoimmune_condition<-survfit(Surv(time, status) ~ a_autoimmune_condition,
data = r.healthy.diagnosis)
ggsurv.t1.a_autoimmune_condition<- list(
ggsurvplot(survfit.t1.a_autoimmune_condition, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t1.a_autoimmune_condition, fun = "cloglog", conf.int =TRUE))
ggsurv.t1.a_autoimmune_condition <-arrange_ggsurvplots(ggsurv.t1.a_autoimmune_condition, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t1.a_autoimmune_condition.png",ggsurv.t1.a_autoimmune_condition,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t1.a_autoimmune_condition.png")
at.risk.t1.a_autoimmune_condition<-ggsurvplot(survfit.t1.a_autoimmune_condition, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
mutate(date=as.Date("29/02/2020",
"%d/%m/%y")+time) %>%
select(date, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t1.a_autoimmune_condition,
col.names = c("Date", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Date | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 2020-02-29 | a_autoimmune_condition=0 | 5,350,070 | 0 | 0 |
| 2020-02-29 | a_autoimmune_condition=1 | 277,450 | 0 | 0 |
| 2020-03-05 | a_autoimmune_condition=0 | 5,348,777 | 172 | 1,414 |
| 2020-03-05 | a_autoimmune_condition=1 | 277,354 | 12 | 108 |
| 2020-03-10 | a_autoimmune_condition=0 | 5,347,098 | 909 | 2,574 |
| 2020-03-10 | a_autoimmune_condition=1 | 277,201 | 67 | 214 |
| 2020-03-15 | a_autoimmune_condition=0 | 5,342,174 | 4,087 | 4,455 |
| 2020-03-15 | a_autoimmune_condition=1 | 276,816 | 285 | 400 |
| 2020-03-20 | a_autoimmune_condition=0 | 5,325,892 | 21,764 | 6,755 |
| 2020-03-20 | a_autoimmune_condition=1 | 275,676 | 1,438 | 634 |
| 2020-03-25 | a_autoimmune_condition=0 | 5,306,825 | 38,662 | 9,185 |
| 2020-03-25 | a_autoimmune_condition=1 | 274,343 | 2,532 | 880 |
| 2020-03-30 | a_autoimmune_condition=0 | 5,291,709 | 52,083 | 11,362 |
| 2020-03-30 | a_autoimmune_condition=1 | 273,258 | 3,436 | 1,105 |
| 2020-04-04 | a_autoimmune_condition=0 | 5,274,026 | 63,692 | 13,691 |
| 2020-04-04 | a_autoimmune_condition=1 | 271,950 | 4,242 | 1,353 |
| 2020-04-09 | a_autoimmune_condition=0 | 5,263,311 | 73,886 | 15,577 |
| 2020-04-09 | a_autoimmune_condition=1 | 271,172 | 4,939 | 1,568 |
| 2020-04-14 | a_autoimmune_condition=0 | 5,255,822 | 80,652 | 16,481 |
| 2020-04-14 | a_autoimmune_condition=1 | 270,594 | 5,414 | 1,668 |
| 2020-04-19 | a_autoimmune_condition=0 | 5,244,775 | 87,431 | 18,510 |
| 2020-04-19 | a_autoimmune_condition=1 | 269,651 | 5,968 | 1,883 |
| 2020-04-24 | a_autoimmune_condition=0 | 5,236,645 | 94,482 | 20,458 |
| 2020-04-24 | a_autoimmune_condition=1 | 269,066 | 6,415 | 2,069 |
| 2020-04-29 | a_autoimmune_condition=0 | 5,231,350 | 98,489 | 22,004 |
| 2020-04-29 | a_autoimmune_condition=1 | 268,710 | 6,664 | 2,219 |
| 2020-05-04 | a_autoimmune_condition=0 | 5,227,423 | 100,942 | 22,966 |
| 2020-05-04 | a_autoimmune_condition=1 | 268,392 | 6,823 | 2,314 |
By a_chronic_kidney_disease
survfit.t1.a_chronic_kidney_disease<-survfit(Surv(time, status) ~ a_chronic_kidney_disease,
data = r.healthy.diagnosis)
ggsurv.t1.a_chronic_kidney_disease<- list(
ggsurvplot(survfit.t1.a_chronic_kidney_disease, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t1.a_chronic_kidney_disease, fun = "cloglog", conf.int =TRUE))
ggsurv.t1.a_chronic_kidney_disease <-arrange_ggsurvplots(ggsurv.t1.a_chronic_kidney_disease, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t1.a_chronic_kidney_disease.png",ggsurv.t1.a_chronic_kidney_disease,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t1.a_chronic_kidney_disease.png")
at.risk.t1.a_chronic_kidney_disease<-ggsurvplot(survfit.t1.a_chronic_kidney_disease, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
mutate(date=as.Date("29/02/2020",
"%d/%m/%y")+time) %>%
select(date, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t1.a_chronic_kidney_disease,
col.names = c("Date", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Date | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 2020-02-29 | a_chronic_kidney_disease=0 | 5,415,678 | 0 | 0 |
| 2020-02-29 | a_chronic_kidney_disease=1 | 211,842 | 0 | 0 |
| 2020-03-05 | a_chronic_kidney_disease=0 | 5,414,480 | 183 | 1,298 |
| 2020-03-05 | a_chronic_kidney_disease=1 | 211,651 | 1 | 224 |
| 2020-03-10 | a_chronic_kidney_disease=0 | 5,412,861 | 946 | 2,378 |
| 2020-03-10 | a_chronic_kidney_disease=1 | 211,438 | 30 | 410 |
| 2020-03-15 | a_chronic_kidney_disease=0 | 5,407,958 | 4,240 | 4,127 |
| 2020-03-15 | a_chronic_kidney_disease=1 | 211,032 | 132 | 728 |
| 2020-03-20 | a_chronic_kidney_disease=0 | 5,391,344 | 22,576 | 6,122 |
| 2020-03-20 | a_chronic_kidney_disease=1 | 210,224 | 626 | 1,267 |
| 2020-03-25 | a_chronic_kidney_disease=0 | 5,372,036 | 39,879 | 8,326 |
| 2020-03-25 | a_chronic_kidney_disease=1 | 209,132 | 1,315 | 1,739 |
| 2020-03-30 | a_chronic_kidney_disease=0 | 5,357,039 | 53,475 | 10,248 |
| 2020-03-30 | a_chronic_kidney_disease=1 | 207,928 | 2,044 | 2,219 |
| 2020-04-04 | a_chronic_kidney_disease=0 | 5,339,574 | 65,132 | 12,201 |
| 2020-04-04 | a_chronic_kidney_disease=1 | 206,402 | 2,802 | 2,843 |
| 2020-04-09 | a_chronic_kidney_disease=0 | 5,329,185 | 75,264 | 13,801 |
| 2020-04-09 | a_chronic_kidney_disease=1 | 205,298 | 3,561 | 3,344 |
| 2020-04-14 | a_chronic_kidney_disease=0 | 5,322,027 | 81,942 | 14,563 |
| 2020-04-14 | a_chronic_kidney_disease=1 | 204,389 | 4,124 | 3,586 |
| 2020-04-19 | a_chronic_kidney_disease=0 | 5,311,447 | 88,577 | 16,243 |
| 2020-04-19 | a_chronic_kidney_disease=1 | 202,979 | 4,822 | 4,150 |
| 2020-04-24 | a_chronic_kidney_disease=0 | 5,303,686 | 95,550 | 17,873 |
| 2020-04-24 | a_chronic_kidney_disease=1 | 202,025 | 5,347 | 4,654 |
| 2020-04-29 | a_chronic_kidney_disease=0 | 5,298,621 | 99,514 | 19,181 |
| 2020-04-29 | a_chronic_kidney_disease=1 | 201,439 | 5,639 | 5,042 |
| 2020-05-04 | a_chronic_kidney_disease=0 | 5,294,888 | 102,007 | 20,016 |
| 2020-05-04 | a_chronic_kidney_disease=1 | 200,927 | 5,758 | 5,264 |
By a_copd
survfit.t1.a_copd<-survfit(Surv(time, status) ~ a_copd,
data = r.healthy.diagnosis)
ggsurv.t1.a_copd<- list(
ggsurvplot(survfit.t1.a_copd, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t1.a_copd, fun = "cloglog", conf.int =TRUE))
ggsurv.t1.a_copd <-arrange_ggsurvplots(ggsurv.t1.a_copd, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t1.a_copd.png",ggsurv.t1.a_copd,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t1.a_copd.png")
at.risk.t1.a_copd<-ggsurvplot(survfit.t1.a_copd, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
mutate(date=as.Date("29/02/2020",
"%d/%m/%y")+time) %>%
select(date, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t1.a_copd,
col.names = c("Date", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Date | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 2020-02-29 | a_copd=0 | 5,504,775 | 0 | 0 |
| 2020-02-29 | a_copd=1 | 122,745 | 0 | 0 |
| 2020-03-05 | a_copd=0 | 5,503,475 | 181 | 1,419 |
| 2020-03-05 | a_copd=1 | 122,656 | 3 | 103 |
| 2020-03-10 | a_copd=0 | 5,501,747 | 946 | 2,604 |
| 2020-03-10 | a_copd=1 | 122,552 | 30 | 184 |
| 2020-03-15 | a_copd=0 | 5,496,698 | 4,274 | 4,475 |
| 2020-03-15 | a_copd=1 | 122,292 | 98 | 380 |
| 2020-03-20 | a_copd=0 | 5,479,797 | 22,732 | 6,757 |
| 2020-03-20 | a_copd=1 | 121,771 | 470 | 632 |
| 2020-03-25 | a_copd=0 | 5,459,959 | 40,346 | 9,210 |
| 2020-03-25 | a_copd=1 | 121,209 | 848 | 855 |
| 2020-03-30 | a_copd=0 | 5,444,340 | 54,321 | 11,398 |
| 2020-03-30 | a_copd=1 | 120,627 | 1,198 | 1,069 |
| 2020-04-04 | a_copd=0 | 5,426,001 | 66,389 | 13,727 |
| 2020-04-04 | a_copd=1 | 119,975 | 1,545 | 1,317 |
| 2020-04-09 | a_copd=0 | 5,414,970 | 76,982 | 15,626 |
| 2020-04-09 | a_copd=1 | 119,513 | 1,843 | 1,519 |
| 2020-04-14 | a_copd=0 | 5,407,253 | 83,980 | 16,534 |
| 2020-04-14 | a_copd=1 | 119,163 | 2,086 | 1,615 |
| 2020-04-19 | a_copd=0 | 5,395,840 | 91,026 | 18,560 |
| 2020-04-19 | a_copd=1 | 118,586 | 2,373 | 1,833 |
| 2020-04-24 | a_copd=0 | 5,387,537 | 98,272 | 20,507 |
| 2020-04-24 | a_copd=1 | 118,174 | 2,625 | 2,020 |
| 2020-04-29 | a_copd=0 | 5,382,126 | 102,404 | 22,044 |
| 2020-04-29 | a_copd=1 | 117,934 | 2,749 | 2,179 |
| 2020-05-04 | a_copd=0 | 5,378,108 | 104,949 | 23,001 |
| 2020-05-04 | a_copd=1 | 117,707 | 2,816 | 2,279 |
By a_dementia
survfit.t1.a_dementia<-survfit(Surv(time, status) ~ a_dementia,
data = r.healthy.diagnosis)
ggsurv.t1.a_dementia<- list(
ggsurvplot(survfit.t1.a_dementia, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t1.a_dementia, fun = "cloglog", conf.int =TRUE))
ggsurv.t1.a_dementia <-arrange_ggsurvplots(ggsurv.t1.a_dementia, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t1.a_dementia.png",ggsurv.t1.a_dementia,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t1.a_dementia.png")
at.risk.t1.a_dementia<-ggsurvplot(survfit.t1.a_dementia, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
mutate(date=as.Date("29/02/2020",
"%d/%m/%y")+time) %>%
select(date, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t1.a_dementia,
col.names = c("Date", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Date | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 2020-02-29 | a_dementia=0 | 5,570,210 | 0 | 0 |
| 2020-02-29 | a_dementia=1 | 57,310 | 0 | 0 |
| 2020-03-05 | a_dementia=0 | 5,568,939 | 184 | 1,390 |
| 2020-03-05 | a_dementia=1 | 57,192 | 0 | 132 |
| 2020-03-10 | a_dementia=0 | 5,567,221 | 963 | 2,551 |
| 2020-03-10 | a_dementia=1 | 57,078 | 13 | 237 |
| 2020-03-15 | a_dementia=0 | 5,562,101 | 4,341 | 4,453 |
| 2020-03-15 | a_dementia=1 | 56,889 | 31 | 402 |
| 2020-03-20 | a_dementia=0 | 5,544,920 | 23,041 | 6,779 |
| 2020-03-20 | a_dementia=1 | 56,648 | 161 | 610 |
| 2020-03-25 | a_dementia=0 | 5,524,879 | 40,777 | 9,290 |
| 2020-03-25 | a_dementia=1 | 56,289 | 417 | 775 |
| 2020-03-30 | a_dementia=0 | 5,509,252 | 54,676 | 11,522 |
| 2020-03-30 | a_dementia=1 | 55,715 | 843 | 945 |
| 2020-04-04 | a_dementia=0 | 5,491,324 | 66,446 | 13,741 |
| 2020-04-04 | a_dementia=1 | 54,652 | 1,488 | 1,303 |
| 2020-04-09 | a_dementia=0 | 5,480,691 | 76,640 | 15,503 |
| 2020-04-09 | a_dementia=1 | 53,792 | 2,185 | 1,642 |
| 2020-04-14 | a_dementia=0 | 5,473,567 | 83,174 | 16,316 |
| 2020-04-14 | a_dementia=1 | 52,849 | 2,892 | 1,833 |
| 2020-04-19 | a_dementia=0 | 5,463,121 | 89,610 | 18,066 |
| 2020-04-19 | a_dementia=1 | 51,305 | 3,789 | 2,327 |
| 2020-04-24 | a_dementia=0 | 5,455,345 | 96,578 | 19,763 |
| 2020-04-24 | a_dementia=1 | 50,366 | 4,319 | 2,764 |
| 2020-04-29 | a_dementia=0 | 5,450,193 | 100,547 | 21,145 |
| 2020-04-29 | a_dementia=1 | 49,867 | 4,606 | 3,078 |
| 2020-05-04 | a_dementia=0 | 5,446,370 | 103,043 | 22,042 |
| 2020-05-04 | a_dementia=1 | 49,445 | 4,722 | 3,238 |
By a_heart_disease
survfit.t1.a_heart_disease<-survfit(Surv(time, status) ~ a_heart_disease,
data = r.healthy.diagnosis)
ggsurv.t1.a_heart_disease<- list(
ggsurvplot(survfit.t1.a_heart_disease, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t1.a_heart_disease, fun = "cloglog", conf.int =TRUE))
ggsurv.t1.a_heart_disease <-arrange_ggsurvplots(ggsurv.t1.a_heart_disease, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t1.a_heart_disease.png",ggsurv.t1.a_heart_disease,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t1.a_heart_disease.png")
at.risk.t1.a_heart_disease<-ggsurvplot(survfit.t1.a_heart_disease, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
mutate(date=as.Date("29/02/2020",
"%d/%m/%y")+time) %>%
select(date, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t1.a_heart_disease,
col.names = c("Date", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Date | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 2020-02-29 | a_heart_disease=0 | 5,079,725 | 0 | 0 |
| 2020-02-29 | a_heart_disease=1 | 547,795 | 0 | 0 |
| 2020-03-05 | a_heart_disease=0 | 5,078,692 | 173 | 1,117 |
| 2020-03-05 | a_heart_disease=1 | 547,439 | 11 | 405 |
| 2020-03-10 | a_heart_disease=0 | 5,077,250 | 858 | 2,073 |
| 2020-03-10 | a_heart_disease=1 | 547,049 | 118 | 715 |
| 2020-03-15 | a_heart_disease=0 | 5,072,893 | 3,908 | 3,504 |
| 2020-03-15 | a_heart_disease=1 | 546,097 | 464 | 1,351 |
| 2020-03-20 | a_heart_disease=0 | 5,057,540 | 21,043 | 5,177 |
| 2020-03-20 | a_heart_disease=1 | 544,028 | 2,159 | 2,212 |
| 2020-03-25 | a_heart_disease=0 | 5,039,789 | 37,170 | 6,956 |
| 2020-03-25 | a_heart_disease=1 | 541,379 | 4,024 | 3,109 |
| 2020-03-30 | a_heart_disease=0 | 5,026,102 | 49,787 | 8,563 |
| 2020-03-30 | a_heart_disease=1 | 538,865 | 5,732 | 3,904 |
| 2020-04-04 | a_heart_disease=0 | 5,010,132 | 60,518 | 10,157 |
| 2020-04-04 | a_heart_disease=1 | 535,844 | 7,416 | 4,887 |
| 2020-04-09 | a_heart_disease=0 | 5,000,662 | 69,928 | 11,474 |
| 2020-04-09 | a_heart_disease=1 | 533,821 | 8,897 | 5,671 |
| 2020-04-14 | a_heart_disease=0 | 4,994,212 | 76,056 | 12,082 |
| 2020-04-14 | a_heart_disease=1 | 532,204 | 10,010 | 6,067 |
| 2020-04-19 | a_heart_disease=0 | 4,984,742 | 82,069 | 13,439 |
| 2020-04-19 | a_heart_disease=1 | 529,684 | 11,330 | 6,954 |
| 2020-04-24 | a_heart_disease=0 | 4,977,727 | 88,469 | 14,820 |
| 2020-04-24 | a_heart_disease=1 | 527,984 | 12,428 | 7,707 |
| 2020-04-29 | a_heart_disease=0 | 4,973,141 | 92,108 | 15,894 |
| 2020-04-29 | a_heart_disease=1 | 526,919 | 13,045 | 8,329 |
| 2020-05-04 | a_heart_disease=0 | 4,969,819 | 94,434 | 16,611 |
| 2020-05-04 | a_heart_disease=1 | 525,996 | 13,331 | 8,669 |
By a_hyperlipidemia
survfit.t1.a_hyperlipidemia<-survfit(Surv(time, status) ~ a_hyperlipidemia,
data = r.healthy.diagnosis)
ggsurv.t1.a_hyperlipidemia<- list(
ggsurvplot(survfit.t1.a_hyperlipidemia, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t1.a_hyperlipidemia, fun = "cloglog", conf.int =TRUE))
ggsurv.t1.a_hyperlipidemia <-arrange_ggsurvplots(ggsurv.t1.a_hyperlipidemia, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t1.a_hyperlipidemia.png",ggsurv.t1.a_hyperlipidemia,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t1.a_hyperlipidemia.png")
at.risk.t1.a_hyperlipidemia<-ggsurvplot(survfit.t1.a_hyperlipidemia, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
mutate(date=as.Date("29/02/2020",
"%d/%m/%y")+time) %>%
select(date, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t1.a_hyperlipidemia,
col.names = c("Date", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Date | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 2020-02-29 | a_hyperlipidemia=0 | 5,106,683 | 0 | 0 |
| 2020-02-29 | a_hyperlipidemia=1 | 520,837 | 0 | 0 |
| 2020-03-05 | a_hyperlipidemia=0 | 5,105,416 | 171 | 1,387 |
| 2020-03-05 | a_hyperlipidemia=1 | 520,715 | 13 | 135 |
| 2020-03-10 | a_hyperlipidemia=0 | 5,103,770 | 878 | 2,532 |
| 2020-03-10 | a_hyperlipidemia=1 | 520,529 | 98 | 256 |
| 2020-03-15 | a_hyperlipidemia=0 | 5,098,984 | 3,925 | 4,373 |
| 2020-03-15 | a_hyperlipidemia=1 | 520,006 | 447 | 482 |
| 2020-03-20 | a_hyperlipidemia=0 | 5,083,527 | 20,762 | 6,504 |
| 2020-03-20 | a_hyperlipidemia=1 | 518,041 | 2,440 | 885 |
| 2020-03-25 | a_hyperlipidemia=0 | 5,065,469 | 36,833 | 8,708 |
| 2020-03-25 | a_hyperlipidemia=1 | 515,699 | 4,361 | 1,357 |
| 2020-03-30 | a_hyperlipidemia=0 | 5,051,255 | 49,540 | 10,693 |
| 2020-03-30 | a_hyperlipidemia=1 | 513,712 | 5,979 | 1,774 |
| 2020-04-04 | a_hyperlipidemia=0 | 5,034,476 | 60,549 | 12,923 |
| 2020-04-04 | a_hyperlipidemia=1 | 511,500 | 7,385 | 2,121 |
| 2020-04-09 | a_hyperlipidemia=0 | 5,024,309 | 70,248 | 14,726 |
| 2020-04-09 | a_hyperlipidemia=1 | 510,174 | 8,577 | 2,419 |
| 2020-04-14 | a_hyperlipidemia=0 | 5,017,120 | 76,737 | 15,593 |
| 2020-04-14 | a_hyperlipidemia=1 | 509,296 | 9,329 | 2,556 |
| 2020-04-19 | a_hyperlipidemia=0 | 5,006,528 | 83,211 | 17,554 |
| 2020-04-19 | a_hyperlipidemia=1 | 507,898 | 10,188 | 2,839 |
| 2020-04-24 | a_hyperlipidemia=0 | 4,998,802 | 89,906 | 19,422 |
| 2020-04-24 | a_hyperlipidemia=1 | 506,909 | 10,991 | 3,105 |
| 2020-04-29 | a_hyperlipidemia=0 | 4,993,771 | 93,688 | 20,907 |
| 2020-04-29 | a_hyperlipidemia=1 | 506,289 | 11,465 | 3,316 |
| 2020-05-04 | a_hyperlipidemia=0 | 4,989,987 | 96,042 | 21,850 |
| 2020-05-04 | a_hyperlipidemia=1 | 505,828 | 11,723 | 3,430 |
By a_hypertension
survfit.t1.a_hypertension<-survfit(Surv(time, status) ~ a_hypertension,
data = r.healthy.diagnosis)
ggsurv.t1.a_hypertension<- list(
ggsurvplot(survfit.t1.a_hypertension, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t1.a_hypertension, fun = "cloglog", conf.int =TRUE))
ggsurv.t1.a_hypertension <-arrange_ggsurvplots(ggsurv.t1.a_hypertension, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t1.a_hypertension.png",ggsurv.t1.a_hypertension,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t1.a_hypertension.png")
at.risk.t1.a_hypertension<-ggsurvplot(survfit.t1.a_hypertension, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
mutate(date=as.Date("29/02/2020",
"%d/%m/%y")+time) %>%
select(date, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t1.a_hypertension,
col.names = c("Date", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Date | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 2020-02-29 | a_hypertension=0 | 4,924,353 | 0 | 0 |
| 2020-02-29 | a_hypertension=1 | 703,167 | 0 | 0 |
| 2020-03-05 | a_hypertension=0 | 4,923,233 | 155 | 1,236 |
| 2020-03-05 | a_hypertension=1 | 702,898 | 29 | 286 |
| 2020-03-10 | a_hypertension=0 | 4,921,708 | 817 | 2,276 |
| 2020-03-10 | a_hypertension=1 | 702,591 | 159 | 512 |
| 2020-03-15 | a_hypertension=0 | 4,917,288 | 3,730 | 3,896 |
| 2020-03-15 | a_hypertension=1 | 701,702 | 642 | 959 |
| 2020-03-20 | a_hypertension=0 | 4,902,358 | 20,171 | 5,728 |
| 2020-03-20 | a_hypertension=1 | 699,210 | 3,031 | 1,661 |
| 2020-03-25 | a_hypertension=0 | 4,885,164 | 35,651 | 7,611 |
| 2020-03-25 | a_hypertension=1 | 696,004 | 5,543 | 2,454 |
| 2020-03-30 | a_hypertension=0 | 4,871,704 | 47,940 | 9,304 |
| 2020-03-30 | a_hypertension=1 | 693,263 | 7,579 | 3,163 |
| 2020-04-04 | a_hypertension=0 | 4,855,829 | 58,503 | 11,130 |
| 2020-04-04 | a_hypertension=1 | 690,147 | 9,431 | 3,914 |
| 2020-04-09 | a_hypertension=0 | 4,846,380 | 67,689 | 12,658 |
| 2020-04-09 | a_hypertension=1 | 688,103 | 11,136 | 4,487 |
| 2020-04-14 | a_hypertension=0 | 4,839,876 | 73,640 | 13,384 |
| 2020-04-14 | a_hypertension=1 | 686,540 | 12,426 | 4,765 |
| 2020-04-19 | a_hypertension=0 | 4,830,390 | 79,483 | 15,016 |
| 2020-04-19 | a_hypertension=1 | 684,036 | 13,916 | 5,377 |
| 2020-04-24 | a_hypertension=0 | 4,823,371 | 85,742 | 16,558 |
| 2020-04-24 | a_hypertension=1 | 682,340 | 15,155 | 5,969 |
| 2020-04-29 | a_hypertension=0 | 4,818,708 | 89,338 | 17,821 |
| 2020-04-29 | a_hypertension=1 | 681,352 | 15,815 | 6,402 |
| 2020-05-04 | a_hypertension=0 | 4,815,278 | 91,554 | 18,625 |
| 2020-05-04 | a_hypertension=1 | 680,537 | 16,211 | 6,655 |
By a_malignant_neoplasm
survfit.t1.a_malignant_neoplasm<-survfit(Surv(time, status) ~ a_malignant_neoplasm,
data = r.healthy.diagnosis)
ggsurv.t1.a_malignant_neoplasm<- list(
ggsurvplot(survfit.t1.a_malignant_neoplasm, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t1.a_malignant_neoplasm, fun = "cloglog", conf.int =TRUE))
ggsurv.t1.a_malignant_neoplasm <-arrange_ggsurvplots(ggsurv.t1.a_malignant_neoplasm, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t1.a_malignant_neoplasm.png",ggsurv.t1.a_malignant_neoplasm,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t1.a_malignant_neoplasm.png")
at.risk.t1.a_malignant_neoplasm<-ggsurvplot(survfit.t1.a_malignant_neoplasm, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
mutate(date=as.Date("29/02/2020",
"%d/%m/%y")+time) %>%
select(date, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t1.a_malignant_neoplasm,
col.names = c("Date", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Date | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 2020-02-29 | a_malignant_neoplasm=0 | 5,330,111 | 0 | 0 |
| 2020-02-29 | a_malignant_neoplasm=1 | 297,409 | 0 | 0 |
| 2020-03-05 | a_malignant_neoplasm=0 | 5,328,988 | 174 | 1,229 |
| 2020-03-05 | a_malignant_neoplasm=1 | 297,143 | 10 | 293 |
| 2020-03-10 | a_malignant_neoplasm=0 | 5,327,436 | 912 | 2,256 |
| 2020-03-10 | a_malignant_neoplasm=1 | 296,863 | 64 | 532 |
| 2020-03-15 | a_malignant_neoplasm=0 | 5,322,764 | 4,094 | 3,901 |
| 2020-03-15 | a_malignant_neoplasm=1 | 296,226 | 278 | 954 |
| 2020-03-20 | a_malignant_neoplasm=0 | 5,306,486 | 21,992 | 5,909 |
| 2020-03-20 | a_malignant_neoplasm=1 | 295,082 | 1,210 | 1,480 |
| 2020-03-25 | a_malignant_neoplasm=0 | 5,287,532 | 39,003 | 8,058 |
| 2020-03-25 | a_malignant_neoplasm=1 | 293,636 | 2,191 | 2,007 |
| 2020-03-30 | a_malignant_neoplasm=0 | 5,272,714 | 52,470 | 10,013 |
| 2020-03-30 | a_malignant_neoplasm=1 | 292,253 | 3,049 | 2,454 |
| 2020-04-04 | a_malignant_neoplasm=0 | 5,255,239 | 64,059 | 12,038 |
| 2020-04-04 | a_malignant_neoplasm=1 | 290,737 | 3,875 | 3,006 |
| 2020-04-09 | a_malignant_neoplasm=0 | 5,244,805 | 74,228 | 13,729 |
| 2020-04-09 | a_malignant_neoplasm=1 | 289,678 | 4,597 | 3,416 |
| 2020-04-14 | a_malignant_neoplasm=0 | 5,237,487 | 80,977 | 14,525 |
| 2020-04-14 | a_malignant_neoplasm=1 | 288,929 | 5,089 | 3,624 |
| 2020-04-19 | a_malignant_neoplasm=0 | 5,226,756 | 87,722 | 16,257 |
| 2020-04-19 | a_malignant_neoplasm=1 | 287,670 | 5,677 | 4,136 |
| 2020-04-24 | a_malignant_neoplasm=0 | 5,218,863 | 94,719 | 17,962 |
| 2020-04-24 | a_malignant_neoplasm=1 | 286,848 | 6,178 | 4,565 |
| 2020-04-29 | a_malignant_neoplasm=0 | 5,213,776 | 98,711 | 19,254 |
| 2020-04-29 | a_malignant_neoplasm=1 | 286,284 | 6,442 | 4,969 |
| 2020-05-04 | a_malignant_neoplasm=0 | 5,210,049 | 101,192 | 20,111 |
| 2020-05-04 | a_malignant_neoplasm=1 | 285,766 | 6,573 | 5,169 |
By a_obesity.5y
survfit.t1.a_obesity.5y<-survfit(Surv(time, status) ~ a_obesity.5y,
data = r.healthy.diagnosis)
ggsurv.t1.a_obesity.5y<- list(
ggsurvplot(survfit.t1.a_obesity.5y, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t1.a_obesity.5y, fun = "cloglog", conf.int =TRUE))
ggsurv.t1.a_obesity.5y <-arrange_ggsurvplots(ggsurv.t1.a_obesity.5y, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t1.a_obesity.5y.png",ggsurv.t1.a_obesity.5y,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t1.a_obesity.5y.png")
at.risk.t1.a_obesity.5y<-ggsurvplot(survfit.t1.a_obesity.5y, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
mutate(date=as.Date("29/02/2020",
"%d/%m/%y")+time) %>%
select(date, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t1.a_obesity.5y,
col.names = c("Date", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Date | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 2020-02-29 | a_obesity.5y=0 | 4,689,927 | 0 | 0 |
| 2020-02-29 | a_obesity.5y=1 | 937,593 | 0 | 0 |
| 2020-03-05 | a_obesity.5y=0 | 4,688,791 | 147 | 1,256 |
| 2020-03-05 | a_obesity.5y=1 | 937,340 | 37 | 266 |
| 2020-03-10 | a_obesity.5y=0 | 4,687,286 | 786 | 2,302 |
| 2020-03-10 | a_obesity.5y=1 | 937,013 | 190 | 486 |
| 2020-03-15 | a_obesity.5y=0 | 4,683,041 | 3,537 | 3,879 |
| 2020-03-15 | a_obesity.5y=1 | 935,949 | 835 | 976 |
| 2020-03-20 | a_obesity.5y=0 | 4,669,182 | 18,753 | 5,589 |
| 2020-03-20 | a_obesity.5y=1 | 932,386 | 4,449 | 1,800 |
| 2020-03-25 | a_obesity.5y=0 | 4,653,385 | 33,008 | 7,328 |
| 2020-03-25 | a_obesity.5y=1 | 927,783 | 8,186 | 2,737 |
| 2020-03-30 | a_obesity.5y=0 | 4,641,111 | 44,240 | 8,841 |
| 2020-03-30 | a_obesity.5y=1 | 923,856 | 11,279 | 3,626 |
| 2020-04-04 | a_obesity.5y=0 | 4,626,405 | 53,983 | 10,607 |
| 2020-04-04 | a_obesity.5y=1 | 919,571 | 13,951 | 4,437 |
| 2020-04-09 | a_obesity.5y=0 | 4,617,461 | 62,621 | 12,123 |
| 2020-04-09 | a_obesity.5y=1 | 917,022 | 16,204 | 5,022 |
| 2020-04-14 | a_obesity.5y=0 | 4,611,157 | 68,347 | 12,829 |
| 2020-04-14 | a_obesity.5y=1 | 915,259 | 17,719 | 5,320 |
| 2020-04-19 | a_obesity.5y=0 | 4,601,856 | 74,129 | 14,495 |
| 2020-04-19 | a_obesity.5y=1 | 912,570 | 19,270 | 5,898 |
| 2020-04-24 | a_obesity.5y=0 | 4,594,963 | 80,103 | 16,156 |
| 2020-04-24 | a_obesity.5y=1 | 910,748 | 20,794 | 6,371 |
| 2020-04-29 | a_obesity.5y=0 | 4,590,483 | 83,476 | 17,446 |
| 2020-04-29 | a_obesity.5y=1 | 909,577 | 21,677 | 6,777 |
| 2020-05-04 | a_obesity.5y=0 | 4,587,126 | 85,588 | 18,287 |
| 2020-05-04 | a_obesity.5y=1 | 908,689 | 22,177 | 6,993 |
By a_t2_diabetes
survfit.t1.a_t2_diabetes<-survfit(Surv(time, status) ~ a_t2_diabetes,
data = r.healthy.diagnosis)
ggsurv.t1.a_t2_diabetes<- list(
ggsurvplot(survfit.t1.a_t2_diabetes, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t1.a_t2_diabetes, fun = "cloglog", conf.int =TRUE))
ggsurv.t1.a_t2_diabetes <-arrange_ggsurvplots(ggsurv.t1.a_t2_diabetes, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t1.a_t2_diabetes.png",ggsurv.t1.a_t2_diabetes,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t1.a_t2_diabetes.png")
at.risk.t1.a_t2_diabetes<-ggsurvplot(survfit.t1.a_t2_diabetes, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
mutate(date=as.Date("29/02/2020",
"%d/%m/%y")+time) %>%
select(date, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t1.a_t2_diabetes,
col.names = c("Date", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Date | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 2020-02-29 | a_t2_diabetes=0 | 5,302,576 | 0 | 0 |
| 2020-02-29 | a_t2_diabetes=1 | 324,944 | 0 | 0 |
| 2020-03-05 | a_t2_diabetes=0 | 5,301,335 | 178 | 1,349 |
| 2020-03-05 | a_t2_diabetes=1 | 324,796 | 6 | 173 |
| 2020-03-10 | a_t2_diabetes=0 | 5,299,700 | 918 | 2,461 |
| 2020-03-10 | a_t2_diabetes=1 | 324,599 | 58 | 327 |
| 2020-03-15 | a_t2_diabetes=0 | 5,294,891 | 4,110 | 4,201 |
| 2020-03-15 | a_t2_diabetes=1 | 324,099 | 262 | 654 |
| 2020-03-20 | a_t2_diabetes=0 | 5,278,543 | 22,057 | 6,288 |
| 2020-03-20 | a_t2_diabetes=1 | 323,025 | 1,145 | 1,101 |
| 2020-03-25 | a_t2_diabetes=0 | 5,259,662 | 38,935 | 8,454 |
| 2020-03-25 | a_t2_diabetes=1 | 321,506 | 2,259 | 1,611 |
| 2020-03-30 | a_t2_diabetes=0 | 5,244,936 | 52,284 | 10,350 |
| 2020-03-30 | a_t2_diabetes=1 | 320,031 | 3,235 | 2,117 |
| 2020-04-04 | a_t2_diabetes=0 | 5,227,607 | 63,799 | 12,418 |
| 2020-04-04 | a_t2_diabetes=1 | 318,369 | 4,135 | 2,626 |
| 2020-04-09 | a_t2_diabetes=0 | 5,217,221 | 73,888 | 14,097 |
| 2020-04-09 | a_t2_diabetes=1 | 317,262 | 4,937 | 3,048 |
| 2020-04-14 | a_t2_diabetes=0 | 5,209,983 | 80,530 | 14,891 |
| 2020-04-14 | a_t2_diabetes=1 | 316,433 | 5,536 | 3,258 |
| 2020-04-19 | a_t2_diabetes=0 | 5,199,365 | 87,151 | 16,685 |
| 2020-04-19 | a_t2_diabetes=1 | 315,061 | 6,248 | 3,708 |
| 2020-04-24 | a_t2_diabetes=0 | 5,191,510 | 94,056 | 18,462 |
| 2020-04-24 | a_t2_diabetes=1 | 314,201 | 6,841 | 4,065 |
| 2020-04-29 | a_t2_diabetes=0 | 5,186,384 | 98,004 | 19,889 |
| 2020-04-29 | a_t2_diabetes=1 | 313,676 | 7,149 | 4,334 |
| 2020-05-04 | a_t2_diabetes=0 | 5,182,569 | 100,449 | 20,797 |
| 2020-05-04 | a_t2_diabetes=1 | 313,246 | 7,316 | 4,483 |
Transition 2: From general population to hospitalised with COVID-19
overall
survfit.t2<-survfit(Surv(time, status) ~ 1,
data = r.healthy.hospitalised)
ggsurvplot.event<-ggsurvplot(survfit.t2, fun = "event", palette="black")
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.position = "none")
ggsave( "ggsurv.t2.png",print(ggsurvplot.event),
dpi=300,
width = 6, height = 5)
include_graphics("ggsurv.t2.png")
survfit.t2<-survfit(Surv(time, status) ~ 1,
data = r.healthy.hospitalised)
ggsurvplot.event<-ggsurvplot(survfit.t2, fun = "event", conf.int =TRUE)
at.risk.t2<-ggsurvplot(survfit.t2, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
mutate(date=as.Date("29/02/2020",
"%d/%m/%y")+time) %>%
select(date, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t2,
col.names = c("Date", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Date | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|
| 2020-02-29 | 5,627,520 | 0 | 0 |
| 2020-03-05 | 5,626,131 | 50 | 1,656 |
| 2020-03-10 | 5,624,299 | 133 | 3,631 |
| 2020-03-15 | 5,618,990 | 480 | 8,747 |
| 2020-03-20 | 5,601,568 | 1,865 | 28,726 |
| 2020-03-25 | 5,581,168 | 3,664 | 47,595 |
| 2020-03-30 | 5,564,967 | 5,248 | 62,738 |
| 2020-04-04 | 5,545,976 | 6,262 | 76,716 |
| 2020-04-09 | 5,534,483 | 7,124 | 88,846 |
| 2020-04-14 | 5,526,416 | 7,647 | 96,568 |
| 2020-04-19 | 5,514,426 | 7,996 | 105,796 |
| 2020-04-24 | 5,505,711 | 8,276 | 115,148 |
| 2020-04-29 | 5,500,060 | 8,426 | 120,950 |
| 2020-05-04 | 5,495,815 | 8,552 | 124,493 |
By gender
survfit.t2.gender<-survfit(Surv(time, status) ~ gender,
data = r.healthy.hospitalised)
ggsurvplot.event<-ggsurvplot(survfit.t2.gender, fun = "event", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.title = element_blank())
ggsurvplot.cloglog<-ggsurvplot(survfit.t2.gender, fun = "cloglog", conf.int =TRUE)
ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.position = "none")
ggsurv.t2.gender<- list(
ggsurvplot.event,
ggsurvplot.cloglog)
ggsurv.t2.gender <-arrange_ggsurvplots(ggsurv.t2.gender, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t2.gender.png",ggsurv.t2.gender,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t2.gender.png")
at.risk.t2.gender<-ggsurvplot(survfit.t2.gender, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
mutate(date=as.Date("29/02/2020",
"%d/%m/%y")+time) %>%
select(date, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t2.gender,
col.names = c("Date", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Date | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 2020-02-29 | gender=Male | 2,768,246 | 0 | 0 |
| 2020-02-29 | gender=Female | 2,859,274 | 0 | 0 |
| 2020-03-05 | gender=Male | 2,767,555 | 22 | 836 |
| 2020-03-05 | gender=Female | 2,858,576 | 28 | 820 |
| 2020-03-10 | gender=Male | 2,766,634 | 69 | 1,800 |
| 2020-03-10 | gender=Female | 2,857,665 | 64 | 1,831 |
| 2020-03-15 | gender=Male | 2,764,197 | 283 | 4,085 |
| 2020-03-15 | gender=Female | 2,854,793 | 197 | 4,662 |
| 2020-03-20 | gender=Male | 2,756,157 | 1,075 | 13,106 |
| 2020-03-20 | gender=Female | 2,845,411 | 790 | 15,620 |
| 2020-03-25 | gender=Male | 2,746,832 | 2,131 | 21,544 |
| 2020-03-25 | gender=Female | 2,834,336 | 1,533 | 26,051 |
| 2020-03-30 | gender=Male | 2,739,460 | 3,075 | 28,063 |
| 2020-03-30 | gender=Female | 2,825,507 | 2,173 | 34,675 |
| 2020-04-04 | gender=Male | 2,731,513 | 3,646 | 33,679 |
| 2020-04-04 | gender=Female | 2,814,463 | 2,616 | 43,037 |
| 2020-04-09 | gender=Male | 2,726,839 | 4,081 | 38,531 |
| 2020-04-09 | gender=Female | 2,807,644 | 3,043 | 50,315 |
| 2020-04-14 | gender=Male | 2,723,689 | 4,338 | 41,484 |
| 2020-04-14 | gender=Female | 2,802,727 | 3,309 | 55,084 |
| 2020-04-19 | gender=Male | 2,718,947 | 4,516 | 45,039 |
| 2020-04-19 | gender=Female | 2,795,479 | 3,480 | 60,757 |
| 2020-04-24 | gender=Male | 2,715,537 | 4,660 | 48,754 |
| 2020-04-24 | gender=Female | 2,790,174 | 3,616 | 66,394 |
| 2020-04-29 | gender=Male | 2,713,248 | 4,734 | 51,108 |
| 2020-04-29 | gender=Female | 2,786,812 | 3,692 | 69,842 |
| 2020-05-04 | gender=Male | 2,711,351 | 4,799 | 52,716 |
| 2020-05-04 | gender=Female | 2,784,464 | 3,753 | 71,777 |
By age
survfit.t2.age_gr<-survfit(Surv(time, status) ~ age_gr,
data = r.healthy.hospitalised)
ggsurv.t2.age_gr<- list(
ggsurvplot(survfit.t2.age_gr, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t2.age_gr, fun = "cloglog", conf.int =TRUE))
ggsurv.t2.age_gr <-arrange_ggsurvplots(ggsurv.t2.age_gr, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t2.age_gr.png",ggsurv.t2.age_gr,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t2.age_gr.png")
at.risk.t2.age_gr<-ggsurvplot(survfit.t2.age_gr, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
mutate(date=as.Date("29/02/2020",
"%d/%m/%y")+time) %>%
select(date, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t2.age_gr,
col.names = c("Date", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Date | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 2020-02-29 | age_gr=Under 18 | 967,227 | 0 | 0 |
| 2020-02-29 | age_gr=18 to 39 | 1,438,732 | 0 | 0 |
| 2020-02-29 | age_gr=40 to 59 | 1,788,832 | 0 | 0 |
| 2020-02-29 | age_gr=60 to 69 | 617,929 | 0 | 0 |
| 2020-02-29 | age_gr=70 to 79 | 474,369 | 0 | 0 |
| 2020-02-29 | age_gr=80 or older | 340,431 | 0 | 0 |
| 2020-03-05 | age_gr=Under 18 | 967,148 | 0 | 99 |
| 2020-03-05 | age_gr=18 to 39 | 1,438,396 | 3 | 444 |
| 2020-03-05 | age_gr=40 to 59 | 1,788,516 | 13 | 371 |
| 2020-03-05 | age_gr=60 to 69 | 617,797 | 8 | 162 |
| 2020-03-05 | age_gr=70 to 79 | 474,235 | 11 | 147 |
| 2020-03-05 | age_gr=80 or older | 340,039 | 15 | 433 |
| 2020-03-10 | age_gr=Under 18 | 967,057 | 0 | 183 |
| 2020-03-10 | age_gr=18 to 39 | 1,437,927 | 7 | 952 |
| 2020-03-10 | age_gr=40 to 59 | 1,788,016 | 31 | 993 |
| 2020-03-10 | age_gr=60 to 69 | 617,593 | 21 | 377 |
| 2020-03-10 | age_gr=70 to 79 | 474,071 | 34 | 308 |
| 2020-03-10 | age_gr=80 or older | 339,635 | 40 | 818 |
| 2020-03-15 | age_gr=Under 18 | 966,925 | 5 | 309 |
| 2020-03-15 | age_gr=18 to 39 | 1,436,495 | 25 | 2,375 |
| 2020-03-15 | age_gr=40 to 59 | 1,786,021 | 97 | 2,998 |
| 2020-03-15 | age_gr=60 to 69 | 617,051 | 91 | 872 |
| 2020-03-15 | age_gr=70 to 79 | 473,621 | 142 | 702 |
| 2020-03-15 | age_gr=80 or older | 338,877 | 120 | 1,491 |
| 2020-03-20 | age_gr=Under 18 | 966,550 | 8 | 801 |
| 2020-03-20 | age_gr=18 to 39 | 1,431,489 | 88 | 8,385 |
| 2020-03-20 | age_gr=40 to 59 | 1,778,104 | 356 | 12,270 |
| 2020-03-20 | age_gr=60 to 69 | 615,231 | 372 | 2,902 |
| 2020-03-20 | age_gr=70 to 79 | 472,402 | 585 | 1,791 |
| 2020-03-20 | age_gr=80 or older | 337,792 | 456 | 2,577 |
| 2020-03-25 | age_gr=Under 18 | 966,050 | 15 | 1,310 |
| 2020-03-25 | age_gr=18 to 39 | 1,426,062 | 178 | 13,738 |
| 2020-03-25 | age_gr=40 to 59 | 1,769,519 | 724 | 20,479 |
| 2020-03-25 | age_gr=60 to 69 | 612,789 | 754 | 4,961 |
| 2020-03-25 | age_gr=70 to 79 | 470,557 | 1,120 | 3,162 |
| 2020-03-25 | age_gr=80 or older | 336,191 | 873 | 3,945 |
| 2020-03-30 | age_gr=Under 18 | 965,564 | 16 | 1,793 |
| 2020-03-30 | age_gr=18 to 39 | 1,422,131 | 245 | 17,776 |
| 2020-03-30 | age_gr=40 to 59 | 1,763,306 | 1,083 | 26,790 |
| 2020-03-30 | age_gr=60 to 69 | 610,854 | 1,068 | 6,596 |
| 2020-03-30 | age_gr=70 to 79 | 468,883 | 1,596 | 4,256 |
| 2020-03-30 | age_gr=80 or older | 334,229 | 1,240 | 5,527 |
| 2020-04-04 | age_gr=Under 18 | 964,820 | 19 | 2,406 |
| 2020-04-04 | age_gr=18 to 39 | 1,417,358 | 289 | 21,368 |
| 2020-04-04 | age_gr=40 to 59 | 1,756,123 | 1,332 | 31,772 |
| 2020-04-04 | age_gr=60 to 69 | 608,859 | 1,270 | 7,946 |
| 2020-04-04 | age_gr=70 to 79 | 467,358 | 1,833 | 5,358 |
| 2020-04-04 | age_gr=80 or older | 331,458 | 1,519 | 7,866 |
| 2020-04-09 | age_gr=Under 18 | 964,348 | 22 | 3,010 |
| 2020-04-09 | age_gr=18 to 39 | 1,414,606 | 340 | 24,415 |
| 2020-04-09 | age_gr=40 to 59 | 1,752,126 | 1,541 | 36,034 |
| 2020-04-09 | age_gr=60 to 69 | 607,762 | 1,400 | 9,043 |
| 2020-04-09 | age_gr=70 to 79 | 466,393 | 1,995 | 6,239 |
| 2020-04-09 | age_gr=80 or older | 329,248 | 1,826 | 10,105 |
| 2020-04-14 | age_gr=Under 18 | 963,998 | 24 | 3,363 |
| 2020-04-14 | age_gr=18 to 39 | 1,412,804 | 361 | 26,339 |
| 2020-04-14 | age_gr=40 to 59 | 1,749,659 | 1,641 | 38,645 |
| 2020-04-14 | age_gr=60 to 69 | 607,070 | 1,471 | 9,660 |
| 2020-04-14 | age_gr=70 to 79 | 465,724 | 2,089 | 6,784 |
| 2020-04-14 | age_gr=80 or older | 327,161 | 2,061 | 11,777 |
| 2020-04-19 | age_gr=Under 18 | 963,456 | 28 | 3,769 |
| 2020-04-19 | age_gr=18 to 39 | 1,410,279 | 378 | 28,203 |
| 2020-04-19 | age_gr=40 to 59 | 1,746,184 | 1,705 | 41,104 |
| 2020-04-19 | age_gr=60 to 69 | 606,044 | 1,517 | 10,419 |
| 2020-04-19 | age_gr=70 to 79 | 464,575 | 2,159 | 7,700 |
| 2020-04-19 | age_gr=80 or older | 323,888 | 2,209 | 14,601 |
| 2020-04-24 | age_gr=Under 18 | 963,003 | 30 | 4,275 |
| 2020-04-24 | age_gr=18 to 39 | 1,408,187 | 405 | 30,512 |
| 2020-04-24 | age_gr=40 to 59 | 1,743,638 | 1,767 | 43,908 |
| 2020-04-24 | age_gr=60 to 69 | 605,380 | 1,549 | 11,169 |
| 2020-04-24 | age_gr=70 to 79 | 463,869 | 2,200 | 8,449 |
| 2020-04-24 | age_gr=80 or older | 321,634 | 2,325 | 16,835 |
| 2020-04-29 | age_gr=Under 18 | 962,652 | 32 | 4,640 |
| 2020-04-29 | age_gr=18 to 39 | 1,406,826 | 417 | 31,841 |
| 2020-04-29 | age_gr=40 to 59 | 1,741,926 | 1,787 | 45,609 |
| 2020-04-29 | age_gr=60 to 69 | 604,888 | 1,568 | 11,647 |
| 2020-04-29 | age_gr=70 to 79 | 463,397 | 2,231 | 8,932 |
| 2020-04-29 | age_gr=80 or older | 320,371 | 2,391 | 18,281 |
| 2020-05-04 | age_gr=Under 18 | 962,391 | 34 | 4,905 |
| 2020-05-04 | age_gr=18 to 39 | 1,405,894 | 428 | 32,755 |
| 2020-05-04 | age_gr=40 to 59 | 1,740,729 | 1,811 | 46,741 |
| 2020-05-04 | age_gr=60 to 69 | 604,521 | 1,586 | 11,943 |
| 2020-05-04 | age_gr=70 to 79 | 463,006 | 2,247 | 9,209 |
| 2020-05-04 | age_gr=80 or older | 319,274 | 2,446 | 18,940 |
By charlson
survfit.t2.charlson<-survfit(Surv(time, status) ~ charlson,
data = r.healthy.hospitalised)
ggsurv.t2.charlson<- list(
ggsurvplot(survfit.t2.charlson, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t2.charlson, fun = "cloglog", conf.int =TRUE))
ggsurv.t2.charlson <-arrange_ggsurvplots(ggsurv.t2.charlson, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t2.charlson.png",ggsurv.t2.charlson,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t2.charlson.png")
at.risk.t2.charlson<-ggsurvplot(survfit.t2.charlson, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
mutate(date=as.Date("29/02/2020",
"%d/%m/%y")+time) %>%
select(date, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t2.charlson,
col.names = c("Date", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Date | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 2020-02-29 | charlson=0 | 4,572,265 | 0 | 0 |
| 2020-02-29 | charlson=1 | 410,497 | 0 | 0 |
| 2020-02-29 | charlson=2 | 363,057 | 0 | 0 |
| 2020-02-29 | charlson=3+ | 281,701 | 0 | 0 |
| 2020-03-05 | charlson=0 | 4,571,509 | 19 | 947 |
| 2020-03-05 | charlson=1 | 410,364 | 9 | 153 |
| 2020-03-05 | charlson=2 | 362,897 | 8 | 181 |
| 2020-03-05 | charlson=3+ | 281,361 | 14 | 375 |
| 2020-03-10 | charlson=0 | 4,570,392 | 44 | 2,217 |
| 2020-03-10 | charlson=1 | 410,196 | 21 | 332 |
| 2020-03-10 | charlson=2 | 362,693 | 29 | 373 |
| 2020-03-10 | charlson=3+ | 281,018 | 39 | 709 |
| 2020-03-15 | charlson=0 | 4,566,799 | 174 | 5,783 |
| 2020-03-15 | charlson=1 | 409,735 | 87 | 756 |
| 2020-03-15 | charlson=2 | 362,184 | 85 | 844 |
| 2020-03-15 | charlson=3+ | 280,272 | 134 | 1,364 |
| 2020-03-20 | charlson=0 | 4,553,390 | 804 | 21,523 |
| 2020-03-20 | charlson=1 | 408,137 | 256 | 2,514 |
| 2020-03-20 | charlson=2 | 360,932 | 316 | 2,180 |
| 2020-03-20 | charlson=3+ | 279,109 | 489 | 2,509 |
| 2020-03-25 | charlson=0 | 4,538,019 | 1,658 | 36,082 |
| 2020-03-25 | charlson=1 | 406,294 | 500 | 4,188 |
| 2020-03-25 | charlson=2 | 359,276 | 639 | 3,549 |
| 2020-03-25 | charlson=3+ | 277,579 | 867 | 3,776 |
| 2020-03-30 | charlson=0 | 4,526,633 | 2,406 | 47,265 |
| 2020-03-30 | charlson=1 | 404,704 | 698 | 5,583 |
| 2020-03-30 | charlson=2 | 357,781 | 919 | 4,774 |
| 2020-03-30 | charlson=3+ | 275,849 | 1,225 | 5,116 |
| 2020-04-04 | charlson=0 | 4,513,349 | 2,881 | 56,835 |
| 2020-04-04 | charlson=1 | 402,809 | 826 | 7,017 |
| 2020-04-04 | charlson=2 | 356,155 | 1,086 | 5,986 |
| 2020-04-04 | charlson=3+ | 273,663 | 1,469 | 6,878 |
| 2020-04-09 | charlson=0 | 4,505,766 | 3,231 | 65,061 |
| 2020-04-09 | charlson=1 | 401,582 | 958 | 8,287 |
| 2020-04-09 | charlson=2 | 355,075 | 1,243 | 7,067 |
| 2020-04-09 | charlson=3+ | 272,060 | 1,692 | 8,431 |
| 2020-04-14 | charlson=0 | 4,500,816 | 3,407 | 70,144 |
| 2020-04-14 | charlson=1 | 400,626 | 1,028 | 9,192 |
| 2020-04-14 | charlson=2 | 354,220 | 1,343 | 7,764 |
| 2020-04-14 | charlson=3+ | 270,754 | 1,869 | 9,468 |
| 2020-04-19 | charlson=0 | 4,493,753 | 3,525 | 75,354 |
| 2020-04-19 | charlson=1 | 399,158 | 1,073 | 10,354 |
| 2020-04-19 | charlson=2 | 352,951 | 1,412 | 8,784 |
| 2020-04-19 | charlson=3+ | 268,564 | 1,986 | 11,304 |
| 2020-04-24 | charlson=0 | 4,488,361 | 3,641 | 81,256 |
| 2020-04-24 | charlson=1 | 398,135 | 1,111 | 11,425 |
| 2020-04-24 | charlson=2 | 352,068 | 1,461 | 9,699 |
| 2020-04-24 | charlson=3+ | 267,147 | 2,063 | 12,768 |
| 2020-04-29 | charlson=0 | 4,484,759 | 3,688 | 84,822 |
| 2020-04-29 | charlson=1 | 397,529 | 1,134 | 12,081 |
| 2020-04-29 | charlson=2 | 351,520 | 1,492 | 10,292 |
| 2020-04-29 | charlson=3+ | 266,252 | 2,112 | 13,755 |
| 2020-05-04 | charlson=0 | 4,482,261 | 3,740 | 87,192 |
| 2020-05-04 | charlson=1 | 397,044 | 1,153 | 12,433 |
| 2020-05-04 | charlson=2 | 351,026 | 1,512 | 10,633 |
| 2020-05-04 | charlson=3+ | 265,484 | 2,147 | 14,235 |
By a_autoimmune_condition
survfit.t2.a_autoimmune_condition<-survfit(Surv(time, status) ~ a_autoimmune_condition,
data = r.healthy.hospitalised)
ggsurv.t2.a_autoimmune_condition<- list(
ggsurvplot(survfit.t2.a_autoimmune_condition, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t2.a_autoimmune_condition, fun = "cloglog", conf.int =TRUE))
ggsurv.t2.a_autoimmune_condition <-arrange_ggsurvplots(ggsurv.t2.a_autoimmune_condition, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t2.a_autoimmune_condition.png",ggsurv.t2.a_autoimmune_condition,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t2.a_autoimmune_condition.png")
at.risk.t2.a_autoimmune_condition<-ggsurvplot(survfit.t2.a_autoimmune_condition, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
mutate(date=as.Date("29/02/2020",
"%d/%m/%y")+time) %>%
select(date, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t2.a_autoimmune_condition,
col.names = c("Date", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Date | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 2020-02-29 | a_autoimmune_condition=0 | 5,350,070 | 0 | 0 |
| 2020-02-29 | a_autoimmune_condition=1 | 277,450 | 0 | 0 |
| 2020-03-05 | a_autoimmune_condition=0 | 5,348,777 | 42 | 1,544 |
| 2020-03-05 | a_autoimmune_condition=1 | 277,354 | 8 | 112 |
| 2020-03-10 | a_autoimmune_condition=0 | 5,347,098 | 116 | 3,367 |
| 2020-03-10 | a_autoimmune_condition=1 | 277,201 | 17 | 264 |
| 2020-03-15 | a_autoimmune_condition=0 | 5,342,174 | 425 | 8,117 |
| 2020-03-15 | a_autoimmune_condition=1 | 276,816 | 55 | 630 |
| 2020-03-20 | a_autoimmune_condition=0 | 5,325,892 | 1,665 | 26,854 |
| 2020-03-20 | a_autoimmune_condition=1 | 275,676 | 200 | 1,872 |
| 2020-03-25 | a_autoimmune_condition=0 | 5,306,825 | 3,287 | 44,560 |
| 2020-03-25 | a_autoimmune_condition=1 | 274,343 | 377 | 3,035 |
| 2020-03-30 | a_autoimmune_condition=0 | 5,291,709 | 4,733 | 58,712 |
| 2020-03-30 | a_autoimmune_condition=1 | 273,258 | 515 | 4,026 |
| 2020-04-04 | a_autoimmune_condition=0 | 5,274,026 | 5,640 | 71,743 |
| 2020-04-04 | a_autoimmune_condition=1 | 271,950 | 622 | 4,973 |
| 2020-04-09 | a_autoimmune_condition=0 | 5,263,311 | 6,413 | 83,050 |
| 2020-04-09 | a_autoimmune_condition=1 | 271,172 | 711 | 5,796 |
| 2020-04-14 | a_autoimmune_condition=0 | 5,255,822 | 6,882 | 90,251 |
| 2020-04-14 | a_autoimmune_condition=1 | 270,594 | 765 | 6,317 |
| 2020-04-19 | a_autoimmune_condition=0 | 5,244,775 | 7,192 | 98,749 |
| 2020-04-19 | a_autoimmune_condition=1 | 269,651 | 804 | 7,047 |
| 2020-04-24 | a_autoimmune_condition=0 | 5,236,645 | 7,432 | 107,508 |
| 2020-04-24 | a_autoimmune_condition=1 | 269,066 | 844 | 7,640 |
| 2020-04-29 | a_autoimmune_condition=0 | 5,231,350 | 7,568 | 112,925 |
| 2020-04-29 | a_autoimmune_condition=1 | 268,710 | 858 | 8,025 |
| 2020-05-04 | a_autoimmune_condition=0 | 5,227,423 | 7,684 | 116,224 |
| 2020-05-04 | a_autoimmune_condition=1 | 268,392 | 868 | 8,269 |
By a_chronic_kidney_disease
survfit.t2.a_chronic_kidney_disease<-survfit(Surv(time, status) ~ a_chronic_kidney_disease,
data = r.healthy.hospitalised)
ggsurv.t2.a_chronic_kidney_disease<- list(
ggsurvplot(survfit.t2.a_chronic_kidney_disease, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t2.a_chronic_kidney_disease, fun = "cloglog", conf.int =TRUE))
ggsurv.t2.a_chronic_kidney_disease <-arrange_ggsurvplots(ggsurv.t2.a_chronic_kidney_disease, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t2.a_chronic_kidney_disease.png",ggsurv.t2.a_chronic_kidney_disease,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t2.a_chronic_kidney_disease.png")
at.risk.t2.a_chronic_kidney_disease<-ggsurvplot(survfit.t2.a_chronic_kidney_disease, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
mutate(date=as.Date("29/02/2020",
"%d/%m/%y")+time) %>%
select(date, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t2.a_chronic_kidney_disease,
col.names = c("Date", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Date | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 2020-02-29 | a_chronic_kidney_disease=0 | 5,415,678 | 0 | 0 |
| 2020-02-29 | a_chronic_kidney_disease=1 | 211,842 | 0 | 0 |
| 2020-03-05 | a_chronic_kidney_disease=0 | 5,414,480 | 41 | 1,440 |
| 2020-03-05 | a_chronic_kidney_disease=1 | 211,651 | 9 | 216 |
| 2020-03-10 | a_chronic_kidney_disease=0 | 5,412,861 | 104 | 3,220 |
| 2020-03-10 | a_chronic_kidney_disease=1 | 211,438 | 29 | 411 |
| 2020-03-15 | a_chronic_kidney_disease=0 | 5,407,958 | 399 | 7,968 |
| 2020-03-15 | a_chronic_kidney_disease=1 | 211,032 | 81 | 779 |
| 2020-03-20 | a_chronic_kidney_disease=0 | 5,391,344 | 1,521 | 27,177 |
| 2020-03-20 | a_chronic_kidney_disease=1 | 210,224 | 344 | 1,549 |
| 2020-03-25 | a_chronic_kidney_disease=0 | 5,372,036 | 3,058 | 45,147 |
| 2020-03-25 | a_chronic_kidney_disease=1 | 209,132 | 606 | 2,448 |
| 2020-03-30 | a_chronic_kidney_disease=0 | 5,357,039 | 4,383 | 59,340 |
| 2020-03-30 | a_chronic_kidney_disease=1 | 207,928 | 865 | 3,398 |
| 2020-04-04 | a_chronic_kidney_disease=0 | 5,339,574 | 5,203 | 72,130 |
| 2020-04-04 | a_chronic_kidney_disease=1 | 206,402 | 1,059 | 4,586 |
| 2020-04-09 | a_chronic_kidney_disease=0 | 5,329,185 | 5,895 | 83,170 |
| 2020-04-09 | a_chronic_kidney_disease=1 | 205,298 | 1,229 | 5,676 |
| 2020-04-14 | a_chronic_kidney_disease=0 | 5,322,027 | 6,303 | 90,202 |
| 2020-04-14 | a_chronic_kidney_disease=1 | 204,389 | 1,344 | 6,366 |
| 2020-04-19 | a_chronic_kidney_disease=0 | 5,311,447 | 6,580 | 98,240 |
| 2020-04-19 | a_chronic_kidney_disease=1 | 202,979 | 1,416 | 7,556 |
| 2020-04-24 | a_chronic_kidney_disease=0 | 5,303,686 | 6,794 | 106,629 |
| 2020-04-24 | a_chronic_kidney_disease=1 | 202,025 | 1,482 | 8,519 |
| 2020-04-29 | a_chronic_kidney_disease=0 | 5,298,621 | 6,913 | 111,782 |
| 2020-04-29 | a_chronic_kidney_disease=1 | 201,439 | 1,513 | 9,168 |
| 2020-05-04 | a_chronic_kidney_disease=0 | 5,294,888 | 7,011 | 115,012 |
| 2020-05-04 | a_chronic_kidney_disease=1 | 200,927 | 1,541 | 9,481 |
By a_copd
survfit.t2.a_copd<-survfit(Surv(time, status) ~ a_copd,
data = r.healthy.hospitalised)
ggsurv.t2.a_copd<- list(
ggsurvplot(survfit.t2.a_copd, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t2.a_copd, fun = "cloglog", conf.int =TRUE))
ggsurv.t2.a_copd <-arrange_ggsurvplots(ggsurv.t2.a_copd, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t2.a_copd.png",ggsurv.t2.a_copd,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t2.a_copd.png")
at.risk.t2.a_copd<-ggsurvplot(survfit.t2.a_copd, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
mutate(date=as.Date("29/02/2020",
"%d/%m/%y")+time) %>%
select(date, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t2.a_copd,
col.names = c("Date", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Date | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 2020-02-29 | a_copd=0 | 5,504,775 | 0 | 0 |
| 2020-02-29 | a_copd=1 | 122,745 | 0 | 0 |
| 2020-03-05 | a_copd=0 | 5,503,475 | 44 | 1,556 |
| 2020-03-05 | a_copd=1 | 122,656 | 6 | 100 |
| 2020-03-10 | a_copd=0 | 5,501,747 | 115 | 3,435 |
| 2020-03-10 | a_copd=1 | 122,552 | 18 | 196 |
| 2020-03-15 | a_copd=0 | 5,496,698 | 409 | 8,340 |
| 2020-03-15 | a_copd=1 | 122,292 | 71 | 407 |
| 2020-03-20 | a_copd=0 | 5,479,797 | 1,655 | 27,834 |
| 2020-03-20 | a_copd=1 | 121,771 | 210 | 892 |
| 2020-03-25 | a_copd=0 | 5,459,959 | 3,309 | 46,247 |
| 2020-03-25 | a_copd=1 | 121,209 | 355 | 1,348 |
| 2020-03-30 | a_copd=0 | 5,444,340 | 4,753 | 60,966 |
| 2020-03-30 | a_copd=1 | 120,627 | 495 | 1,772 |
| 2020-04-04 | a_copd=0 | 5,426,001 | 5,693 | 74,423 |
| 2020-04-04 | a_copd=1 | 119,975 | 569 | 2,293 |
| 2020-04-09 | a_copd=0 | 5,414,970 | 6,481 | 86,127 |
| 2020-04-09 | a_copd=1 | 119,513 | 643 | 2,719 |
| 2020-04-14 | a_copd=0 | 5,407,253 | 6,948 | 93,566 |
| 2020-04-14 | a_copd=1 | 119,163 | 699 | 3,002 |
| 2020-04-19 | a_copd=0 | 5,395,840 | 7,257 | 102,329 |
| 2020-04-19 | a_copd=1 | 118,586 | 739 | 3,467 |
| 2020-04-24 | a_copd=0 | 5,387,537 | 7,510 | 111,269 |
| 2020-04-24 | a_copd=1 | 118,174 | 766 | 3,879 |
| 2020-04-29 | a_copd=0 | 5,382,126 | 7,639 | 116,809 |
| 2020-04-29 | a_copd=1 | 117,934 | 787 | 4,141 |
| 2020-05-04 | a_copd=0 | 5,378,108 | 7,754 | 120,196 |
| 2020-05-04 | a_copd=1 | 117,707 | 798 | 4,297 |
By a_dementia
survfit.t2.a_dementia<-survfit(Surv(time, status) ~ a_dementia,
data = r.healthy.hospitalised)
ggsurv.t2.a_dementia<- list(
ggsurvplot(survfit.t2.a_dementia, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t2.a_dementia, fun = "cloglog", conf.int =TRUE))
ggsurv.t2.a_dementia <-arrange_ggsurvplots(ggsurv.t2.a_dementia, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t2.a_dementia.png",ggsurv.t2.a_dementia,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t2.a_dementia.png")
at.risk.t2.a_dementia<-ggsurvplot(survfit.t2.a_dementia, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
mutate(date=as.Date("29/02/2020",
"%d/%m/%y")+time) %>%
select(date, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t2.a_dementia,
col.names = c("Date", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Date | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 2020-02-29 | a_dementia=0 | 5,570,210 | 0 | 0 |
| 2020-02-29 | a_dementia=1 | 57,310 | 0 | 0 |
| 2020-03-05 | a_dementia=0 | 5,568,939 | 48 | 1,526 |
| 2020-03-05 | a_dementia=1 | 57,192 | 2 | 130 |
| 2020-03-10 | a_dementia=0 | 5,567,221 | 127 | 3,387 |
| 2020-03-10 | a_dementia=1 | 57,078 | 6 | 244 |
| 2020-03-15 | a_dementia=0 | 5,562,101 | 461 | 8,333 |
| 2020-03-15 | a_dementia=1 | 56,889 | 19 | 414 |
| 2020-03-20 | a_dementia=0 | 5,544,920 | 1,777 | 28,043 |
| 2020-03-20 | a_dementia=1 | 56,648 | 88 | 683 |
| 2020-03-25 | a_dementia=0 | 5,524,879 | 3,513 | 46,554 |
| 2020-03-25 | a_dementia=1 | 56,289 | 151 | 1,041 |
| 2020-03-30 | a_dementia=0 | 5,509,252 | 5,025 | 61,173 |
| 2020-03-30 | a_dementia=1 | 55,715 | 223 | 1,565 |
| 2020-04-04 | a_dementia=0 | 5,491,324 | 5,973 | 74,214 |
| 2020-04-04 | a_dementia=1 | 54,652 | 289 | 2,502 |
| 2020-04-09 | a_dementia=0 | 5,480,691 | 6,730 | 85,413 |
| 2020-04-09 | a_dementia=1 | 53,792 | 394 | 3,433 |
| 2020-04-14 | a_dementia=0 | 5,473,567 | 7,181 | 92,309 |
| 2020-04-14 | a_dementia=1 | 52,849 | 466 | 4,259 |
| 2020-04-19 | a_dementia=0 | 5,463,121 | 7,488 | 100,188 |
| 2020-04-19 | a_dementia=1 | 51,305 | 508 | 5,608 |
| 2020-04-24 | a_dementia=0 | 5,455,345 | 7,727 | 108,614 |
| 2020-04-24 | a_dementia=1 | 50,366 | 549 | 6,534 |
| 2020-04-29 | a_dementia=0 | 5,450,193 | 7,852 | 113,840 |
| 2020-04-29 | a_dementia=1 | 49,867 | 574 | 7,110 |
| 2020-05-04 | a_dementia=0 | 5,446,370 | 7,957 | 117,128 |
| 2020-05-04 | a_dementia=1 | 49,445 | 595 | 7,365 |
By a_heart_disease
survfit.t2.a_heart_disease<-survfit(Surv(time, status) ~ a_heart_disease,
data = r.healthy.hospitalised)
ggsurv.t2.a_heart_disease<- list(
ggsurvplot(survfit.t2.a_heart_disease, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t2.a_heart_disease, fun = "cloglog", conf.int =TRUE))
ggsurv.t2.a_heart_disease <-arrange_ggsurvplots(ggsurv.t2.a_heart_disease, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t2.a_heart_disease.png",ggsurv.t2.a_heart_disease,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t2.a_heart_disease.png")
at.risk.t2.a_heart_disease<-ggsurvplot(survfit.t2.a_heart_disease, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
mutate(date=as.Date("29/02/2020",
"%d/%m/%y")+time) %>%
select(date, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t2.a_heart_disease,
col.names = c("Date", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Date | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 2020-02-29 | a_heart_disease=0 | 5,079,725 | 0 | 0 |
| 2020-02-29 | a_heart_disease=1 | 547,795 | 0 | 0 |
| 2020-03-05 | a_heart_disease=0 | 5,078,692 | 31 | 1,259 |
| 2020-03-05 | a_heart_disease=1 | 547,439 | 19 | 397 |
| 2020-03-10 | a_heart_disease=0 | 5,077,250 | 82 | 2,849 |
| 2020-03-10 | a_heart_disease=1 | 547,049 | 51 | 782 |
| 2020-03-15 | a_heart_disease=0 | 5,072,893 | 309 | 7,103 |
| 2020-03-15 | a_heart_disease=1 | 546,097 | 171 | 1,644 |
| 2020-03-20 | a_heart_disease=0 | 5,057,540 | 1,235 | 24,985 |
| 2020-03-20 | a_heart_disease=1 | 544,028 | 630 | 3,741 |
| 2020-03-25 | a_heart_disease=0 | 5,039,789 | 2,477 | 41,649 |
| 2020-03-25 | a_heart_disease=1 | 541,379 | 1,187 | 5,946 |
| 2020-03-30 | a_heart_disease=0 | 5,026,102 | 3,574 | 54,776 |
| 2020-03-30 | a_heart_disease=1 | 538,865 | 1,674 | 7,962 |
| 2020-04-04 | a_heart_disease=0 | 5,010,132 | 4,269 | 66,406 |
| 2020-04-04 | a_heart_disease=1 | 535,844 | 1,993 | 10,310 |
| 2020-04-09 | a_heart_disease=0 | 5,000,662 | 4,831 | 76,571 |
| 2020-04-09 | a_heart_disease=1 | 533,821 | 2,293 | 12,275 |
| 2020-04-14 | a_heart_disease=0 | 4,994,212 | 5,139 | 82,999 |
| 2020-04-14 | a_heart_disease=1 | 532,204 | 2,508 | 13,569 |
| 2020-04-19 | a_heart_disease=0 | 4,984,742 | 5,359 | 90,149 |
| 2020-04-19 | a_heart_disease=1 | 529,684 | 2,637 | 15,647 |
| 2020-04-24 | a_heart_disease=0 | 4,977,727 | 5,549 | 97,740 |
| 2020-04-24 | a_heart_disease=1 | 527,984 | 2,727 | 17,408 |
| 2020-04-29 | a_heart_disease=0 | 4,973,141 | 5,639 | 102,363 |
| 2020-04-29 | a_heart_disease=1 | 526,919 | 2,787 | 18,587 |
| 2020-05-04 | a_heart_disease=0 | 4,969,819 | 5,715 | 105,330 |
| 2020-05-04 | a_heart_disease=1 | 525,996 | 2,837 | 19,163 |
By a_hyperlipidemia
survfit.t2.a_hyperlipidemia<-survfit(Surv(time, status) ~ a_hyperlipidemia,
data = r.healthy.hospitalised)
ggsurv.t2.a_hyperlipidemia<- list(
ggsurvplot(survfit.t2.a_hyperlipidemia, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t2.a_hyperlipidemia, fun = "cloglog", conf.int =TRUE))
ggsurv.t2.a_hyperlipidemia <-arrange_ggsurvplots(ggsurv.t2.a_hyperlipidemia, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t2.a_hyperlipidemia.png",ggsurv.t2.a_hyperlipidemia,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t2.a_hyperlipidemia.png")
at.risk.t2.a_hyperlipidemia<-ggsurvplot(survfit.t2.a_hyperlipidemia, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
mutate(date=as.Date("29/02/2020",
"%d/%m/%y")+time) %>%
select(date, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t2.a_hyperlipidemia,
col.names = c("Date", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Date | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 2020-02-29 | a_hyperlipidemia=0 | 5,106,683 | 0 | 0 |
| 2020-02-29 | a_hyperlipidemia=1 | 520,837 | 0 | 0 |
| 2020-03-05 | a_hyperlipidemia=0 | 5,105,416 | 42 | 1,516 |
| 2020-03-05 | a_hyperlipidemia=1 | 520,715 | 8 | 140 |
| 2020-03-10 | a_hyperlipidemia=0 | 5,103,770 | 113 | 3,297 |
| 2020-03-10 | a_hyperlipidemia=1 | 520,529 | 20 | 334 |
| 2020-03-15 | a_hyperlipidemia=0 | 5,098,984 | 395 | 7,903 |
| 2020-03-15 | a_hyperlipidemia=1 | 520,006 | 85 | 844 |
| 2020-03-20 | a_hyperlipidemia=0 | 5,083,527 | 1,494 | 25,772 |
| 2020-03-20 | a_hyperlipidemia=1 | 518,041 | 371 | 2,954 |
| 2020-03-25 | a_hyperlipidemia=0 | 5,065,469 | 2,932 | 42,609 |
| 2020-03-25 | a_hyperlipidemia=1 | 515,699 | 732 | 4,986 |
| 2020-03-30 | a_hyperlipidemia=0 | 5,051,255 | 4,205 | 56,028 |
| 2020-03-30 | a_hyperlipidemia=1 | 513,712 | 1,043 | 6,710 |
| 2020-04-04 | a_hyperlipidemia=0 | 5,034,476 | 5,053 | 68,419 |
| 2020-04-04 | a_hyperlipidemia=1 | 511,500 | 1,209 | 8,297 |
| 2020-04-09 | a_hyperlipidemia=0 | 5,024,309 | 5,764 | 79,210 |
| 2020-04-09 | a_hyperlipidemia=1 | 510,174 | 1,360 | 9,636 |
| 2020-04-14 | a_hyperlipidemia=0 | 5,017,120 | 6,205 | 86,125 |
| 2020-04-14 | a_hyperlipidemia=1 | 509,296 | 1,442 | 10,443 |
| 2020-04-19 | a_hyperlipidemia=0 | 5,006,528 | 6,503 | 94,262 |
| 2020-04-19 | a_hyperlipidemia=1 | 507,898 | 1,493 | 11,534 |
| 2020-04-24 | a_hyperlipidemia=0 | 4,998,802 | 6,724 | 102,604 |
| 2020-04-24 | a_hyperlipidemia=1 | 506,909 | 1,552 | 12,544 |
| 2020-04-29 | a_hyperlipidemia=0 | 4,993,771 | 6,857 | 107,738 |
| 2020-04-29 | a_hyperlipidemia=1 | 506,289 | 1,569 | 13,212 |
| 2020-05-04 | a_hyperlipidemia=0 | 4,989,987 | 6,965 | 110,927 |
| 2020-05-04 | a_hyperlipidemia=1 | 505,828 | 1,587 | 13,566 |
By a_hypertension
survfit.t2.a_hypertension<-survfit(Surv(time, status) ~ a_hypertension,
data = r.healthy.hospitalised)
ggsurv.t2.a_hypertension<- list(
ggsurvplot(survfit.t2.a_hypertension, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t2.a_hypertension, fun = "cloglog", conf.int =TRUE))
ggsurv.t2.a_hypertension <-arrange_ggsurvplots(ggsurv.t2.a_hypertension, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t2.a_hypertension.png",ggsurv.t2.a_hypertension,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t2.a_hypertension.png")
at.risk.t2.a_hypertension<-ggsurvplot(survfit.t2.a_hypertension, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
mutate(date=as.Date("29/02/2020",
"%d/%m/%y")+time) %>%
select(date, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t2.a_hypertension,
col.names = c("Date", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Date | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 2020-02-29 | a_hypertension=0 | 4,924,353 | 0 | 0 |
| 2020-02-29 | a_hypertension=1 | 703,167 | 0 | 0 |
| 2020-03-05 | a_hypertension=0 | 4,923,233 | 40 | 1,351 |
| 2020-03-05 | a_hypertension=1 | 702,898 | 10 | 305 |
| 2020-03-10 | a_hypertension=0 | 4,921,708 | 97 | 2,996 |
| 2020-03-10 | a_hypertension=1 | 702,591 | 36 | 635 |
| 2020-03-15 | a_hypertension=0 | 4,917,288 | 330 | 7,296 |
| 2020-03-15 | a_hypertension=1 | 701,702 | 150 | 1,451 |
| 2020-03-20 | a_hypertension=0 | 4,902,358 | 1,299 | 24,600 |
| 2020-03-20 | a_hypertension=1 | 699,210 | 566 | 4,126 |
| 2020-03-25 | a_hypertension=0 | 4,885,164 | 2,514 | 40,748 |
| 2020-03-25 | a_hypertension=1 | 696,004 | 1,150 | 6,847 |
| 2020-03-30 | a_hypertension=0 | 4,871,704 | 3,607 | 53,637 |
| 2020-03-30 | a_hypertension=1 | 693,263 | 1,641 | 9,101 |
| 2020-04-04 | a_hypertension=0 | 4,855,829 | 4,340 | 65,293 |
| 2020-04-04 | a_hypertension=1 | 690,147 | 1,922 | 11,423 |
| 2020-04-09 | a_hypertension=0 | 4,846,380 | 4,959 | 75,388 |
| 2020-04-09 | a_hypertension=1 | 688,103 | 2,165 | 13,458 |
| 2020-04-14 | a_hypertension=0 | 4,839,876 | 5,322 | 81,702 |
| 2020-04-14 | a_hypertension=1 | 686,540 | 2,325 | 14,866 |
| 2020-04-19 | a_hypertension=0 | 4,830,390 | 5,562 | 88,937 |
| 2020-04-19 | a_hypertension=1 | 684,036 | 2,434 | 16,859 |
| 2020-04-24 | a_hypertension=0 | 4,823,371 | 5,757 | 96,543 |
| 2020-04-24 | a_hypertension=1 | 682,340 | 2,519 | 18,605 |
| 2020-04-29 | a_hypertension=0 | 4,818,708 | 5,862 | 101,297 |
| 2020-04-29 | a_hypertension=1 | 681,352 | 2,564 | 19,653 |
| 2020-05-04 | a_hypertension=0 | 4,815,278 | 5,948 | 104,231 |
| 2020-05-04 | a_hypertension=1 | 680,537 | 2,604 | 20,262 |
By a_malignant_neoplasm
survfit.t2.a_malignant_neoplasm<-survfit(Surv(time, status) ~ a_malignant_neoplasm,
data = r.healthy.hospitalised)
ggsurv.t2.a_malignant_neoplasm<- list(
ggsurvplot(survfit.t2.a_malignant_neoplasm, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t2.a_malignant_neoplasm, fun = "cloglog", conf.int =TRUE))
ggsurv.t2.a_malignant_neoplasm <-arrange_ggsurvplots(ggsurv.t2.a_malignant_neoplasm, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t2.a_malignant_neoplasm.png",ggsurv.t2.a_malignant_neoplasm,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t2.a_malignant_neoplasm.png")
at.risk.t2.a_malignant_neoplasm<-ggsurvplot(survfit.t2.a_malignant_neoplasm, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
mutate(date=as.Date("29/02/2020",
"%d/%m/%y")+time) %>%
select(date, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t2.a_malignant_neoplasm,
col.names = c("Date", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Date | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 2020-02-29 | a_malignant_neoplasm=0 | 5,330,111 | 0 | 0 |
| 2020-02-29 | a_malignant_neoplasm=1 | 297,409 | 0 | 0 |
| 2020-03-05 | a_malignant_neoplasm=0 | 5,328,988 | 41 | 1,362 |
| 2020-03-05 | a_malignant_neoplasm=1 | 297,143 | 9 | 294 |
| 2020-03-10 | a_malignant_neoplasm=0 | 5,327,436 | 103 | 3,065 |
| 2020-03-10 | a_malignant_neoplasm=1 | 296,863 | 30 | 566 |
| 2020-03-15 | a_malignant_neoplasm=0 | 5,322,764 | 387 | 7,608 |
| 2020-03-15 | a_malignant_neoplasm=1 | 296,226 | 93 | 1,139 |
| 2020-03-20 | a_malignant_neoplasm=0 | 5,306,486 | 1,527 | 26,374 |
| 2020-03-20 | a_malignant_neoplasm=1 | 295,082 | 338 | 2,352 |
| 2020-03-25 | a_malignant_neoplasm=0 | 5,287,532 | 3,022 | 44,039 |
| 2020-03-25 | a_malignant_neoplasm=1 | 293,636 | 642 | 3,556 |
| 2020-03-30 | a_malignant_neoplasm=0 | 5,272,714 | 4,363 | 58,120 |
| 2020-03-30 | a_malignant_neoplasm=1 | 292,253 | 885 | 4,618 |
| 2020-04-04 | a_malignant_neoplasm=0 | 5,255,239 | 5,236 | 70,861 |
| 2020-04-04 | a_malignant_neoplasm=1 | 290,737 | 1,026 | 5,855 |
| 2020-04-09 | a_malignant_neoplasm=0 | 5,244,805 | 5,971 | 81,986 |
| 2020-04-09 | a_malignant_neoplasm=1 | 289,678 | 1,153 | 6,860 |
| 2020-04-14 | a_malignant_neoplasm=0 | 5,237,487 | 6,397 | 89,105 |
| 2020-04-14 | a_malignant_neoplasm=1 | 288,929 | 1,250 | 7,463 |
| 2020-04-19 | a_malignant_neoplasm=0 | 5,226,756 | 6,677 | 97,302 |
| 2020-04-19 | a_malignant_neoplasm=1 | 287,670 | 1,319 | 8,494 |
| 2020-04-24 | a_malignant_neoplasm=0 | 5,218,863 | 6,924 | 105,757 |
| 2020-04-24 | a_malignant_neoplasm=1 | 286,848 | 1,352 | 9,391 |
| 2020-04-29 | a_malignant_neoplasm=0 | 5,213,776 | 7,044 | 110,921 |
| 2020-04-29 | a_malignant_neoplasm=1 | 286,284 | 1,382 | 10,029 |
| 2020-05-04 | a_malignant_neoplasm=0 | 5,210,049 | 7,153 | 114,150 |
| 2020-05-04 | a_malignant_neoplasm=1 | 285,766 | 1,399 | 10,343 |
By a_obesity.5y
survfit.t2.a_obesity.5y<-survfit(Surv(time, status) ~ a_obesity.5y,
data = r.healthy.hospitalised)
ggsurv.t2.a_obesity.5y<- list(
ggsurvplot(survfit.t2.a_obesity.5y, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t2.a_obesity.5y, fun = "cloglog", conf.int =TRUE))
ggsurv.t2.a_obesity.5y <-arrange_ggsurvplots(ggsurv.t2.a_obesity.5y, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t2.a_obesity.5y.png",ggsurv.t2.a_obesity.5y,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t2.a_obesity.5y.png")
at.risk.t2.a_obesity.5y<-ggsurvplot(survfit.t2.a_obesity.5y, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
mutate(date=as.Date("29/02/2020",
"%d/%m/%y")+time) %>%
select(date, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t2.a_obesity.5y,
col.names = c("Date", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Date | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 2020-02-29 | a_obesity.5y=0 | 4,689,927 | 0 | 0 |
| 2020-02-29 | a_obesity.5y=1 | 937,593 | 0 | 0 |
| 2020-03-05 | a_obesity.5y=0 | 4,688,791 | 26 | 1,377 |
| 2020-03-05 | a_obesity.5y=1 | 937,340 | 24 | 279 |
| 2020-03-10 | a_obesity.5y=0 | 4,687,286 | 80 | 3,008 |
| 2020-03-10 | a_obesity.5y=1 | 937,013 | 53 | 623 |
| 2020-03-15 | a_obesity.5y=0 | 4,683,041 | 286 | 7,130 |
| 2020-03-15 | a_obesity.5y=1 | 935,949 | 194 | 1,617 |
| 2020-03-20 | a_obesity.5y=0 | 4,669,182 | 1,103 | 23,239 |
| 2020-03-20 | a_obesity.5y=1 | 932,386 | 762 | 5,487 |
| 2020-03-25 | a_obesity.5y=0 | 4,653,385 | 2,179 | 38,157 |
| 2020-03-25 | a_obesity.5y=1 | 927,783 | 1,485 | 9,438 |
| 2020-03-30 | a_obesity.5y=0 | 4,641,111 | 3,078 | 50,003 |
| 2020-03-30 | a_obesity.5y=1 | 923,856 | 2,170 | 12,735 |
| 2020-04-04 | a_obesity.5y=0 | 4,626,405 | 3,702 | 60,888 |
| 2020-04-04 | a_obesity.5y=1 | 919,571 | 2,560 | 15,828 |
| 2020-04-09 | a_obesity.5y=0 | 4,617,461 | 4,268 | 70,476 |
| 2020-04-09 | a_obesity.5y=1 | 917,022 | 2,856 | 18,370 |
| 2020-04-14 | a_obesity.5y=0 | 4,611,157 | 4,591 | 76,585 |
| 2020-04-14 | a_obesity.5y=1 | 915,259 | 3,056 | 19,983 |
| 2020-04-19 | a_obesity.5y=0 | 4,601,856 | 4,809 | 83,815 |
| 2020-04-19 | a_obesity.5y=1 | 912,570 | 3,187 | 21,981 |
| 2020-04-24 | a_obesity.5y=0 | 4,594,963 | 4,998 | 91,261 |
| 2020-04-24 | a_obesity.5y=1 | 910,748 | 3,278 | 23,887 |
| 2020-04-29 | a_obesity.5y=0 | 4,590,483 | 5,091 | 95,831 |
| 2020-04-29 | a_obesity.5y=1 | 909,577 | 3,335 | 25,119 |
| 2020-05-04 | a_obesity.5y=0 | 4,587,126 | 5,177 | 98,698 |
| 2020-05-04 | a_obesity.5y=1 | 908,689 | 3,375 | 25,795 |
By a_t2_diabetes
survfit.t2.a_t2_diabetes<-survfit(Surv(time, status) ~ a_t2_diabetes,
data = r.healthy.hospitalised)
ggsurv.t2.a_t2_diabetes<- list(
ggsurvplot(survfit.t2.a_t2_diabetes, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t2.a_t2_diabetes, fun = "cloglog", conf.int =TRUE))
ggsurv.t2.a_t2_diabetes <-arrange_ggsurvplots(ggsurv.t2.a_t2_diabetes, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t2.a_t2_diabetes.png",ggsurv.t2.a_t2_diabetes,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t2.a_t2_diabetes.png")
at.risk.t2.a_t2_diabetes<-ggsurvplot(survfit.t2.a_t2_diabetes, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
mutate(date=as.Date("29/02/2020",
"%d/%m/%y")+time) %>%
select(date, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t2.a_t2_diabetes,
col.names = c("Date", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Date | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 2020-02-29 | a_t2_diabetes=0 | 5,302,576 | 0 | 0 |
| 2020-02-29 | a_t2_diabetes=1 | 324,944 | 0 | 0 |
| 2020-03-05 | a_t2_diabetes=0 | 5,301,335 | 43 | 1,484 |
| 2020-03-05 | a_t2_diabetes=1 | 324,796 | 7 | 172 |
| 2020-03-10 | a_t2_diabetes=0 | 5,299,700 | 109 | 3,270 |
| 2020-03-10 | a_t2_diabetes=1 | 324,599 | 24 | 361 |
| 2020-03-15 | a_t2_diabetes=0 | 5,294,891 | 375 | 7,936 |
| 2020-03-15 | a_t2_diabetes=1 | 324,099 | 105 | 811 |
| 2020-03-20 | a_t2_diabetes=0 | 5,278,543 | 1,504 | 26,841 |
| 2020-03-20 | a_t2_diabetes=1 | 323,025 | 361 | 1,885 |
| 2020-03-25 | a_t2_diabetes=0 | 5,259,662 | 2,940 | 44,449 |
| 2020-03-25 | a_t2_diabetes=1 | 321,506 | 724 | 3,146 |
| 2020-03-30 | a_t2_diabetes=0 | 5,244,936 | 4,175 | 58,459 |
| 2020-03-30 | a_t2_diabetes=1 | 320,031 | 1,073 | 4,279 |
| 2020-04-04 | a_t2_diabetes=0 | 5,227,607 | 4,988 | 71,229 |
| 2020-04-04 | a_t2_diabetes=1 | 318,369 | 1,274 | 5,487 |
| 2020-04-09 | a_t2_diabetes=0 | 5,217,221 | 5,669 | 82,316 |
| 2020-04-09 | a_t2_diabetes=1 | 317,262 | 1,455 | 6,530 |
| 2020-04-14 | a_t2_diabetes=0 | 5,209,983 | 6,070 | 89,351 |
| 2020-04-14 | a_t2_diabetes=1 | 316,433 | 1,577 | 7,217 |
| 2020-04-19 | a_t2_diabetes=0 | 5,199,365 | 6,334 | 97,502 |
| 2020-04-19 | a_t2_diabetes=1 | 315,061 | 1,662 | 8,294 |
| 2020-04-24 | a_t2_diabetes=0 | 5,191,510 | 6,561 | 105,957 |
| 2020-04-24 | a_t2_diabetes=1 | 314,201 | 1,715 | 9,191 |
| 2020-04-29 | a_t2_diabetes=0 | 5,186,384 | 6,673 | 111,220 |
| 2020-04-29 | a_t2_diabetes=1 | 313,676 | 1,753 | 9,730 |
| 2020-05-04 | a_t2_diabetes=0 | 5,182,569 | 6,783 | 114,463 |
| 2020-05-04 | a_t2_diabetes=1 | 313,246 | 1,769 | 10,030 |
Transition 3: From general population to death
overall
survfit.t3<-survfit(Surv(time, status) ~ 1,
data = r.healthy.death)
ggsurvplot.event<-ggsurvplot(survfit.t3, fun = "event", palette="black")
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.position = "none")
ggsave( "ggsurv.t3.png",print(ggsurvplot.event),
dpi=300,
width = 6, height = 5)
include_graphics("ggsurv.t3.png")
survfit.t3<-survfit(Surv(time, status) ~ 1,
data = r.healthy.death)
ggsurvplot.event<-ggsurvplot(survfit.t3, fun = "event", conf.int =TRUE)
at.risk.t3<-ggsurvplot(survfit.t3, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
mutate(date=as.Date("29/02/2020",
"%d/%m/%y")+time) %>%
select(date, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t3,
col.names = c("Date", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Date | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|
| 2020-02-29 | 5,627,520 | 0 | 0 |
| 2020-03-05 | 5,626,131 | 600 | 1,106 |
| 2020-03-10 | 5,624,299 | 1,069 | 2,695 |
| 2020-03-15 | 5,618,990 | 1,881 | 7,346 |
| 2020-03-20 | 5,601,568 | 2,600 | 27,991 |
| 2020-03-25 | 5,581,168 | 3,182 | 48,077 |
| 2020-03-30 | 5,564,967 | 3,731 | 64,255 |
| 2020-04-04 | 5,545,976 | 4,993 | 77,985 |
| 2020-04-09 | 5,534,483 | 5,930 | 90,040 |
| 2020-04-14 | 5,526,416 | 6,287 | 97,928 |
| 2020-04-19 | 5,514,426 | 7,752 | 106,040 |
| 2020-04-24 | 5,505,711 | 9,065 | 114,359 |
| 2020-04-29 | 5,500,060 | 10,186 | 119,190 |
| 2020-05-04 | 5,495,815 | 10,738 | 122,307 |
By gender
survfit.t3.gender<-survfit(Surv(time, status) ~ gender,
data = r.healthy.death)
ggsurvplot.event<-ggsurvplot(survfit.t3.gender, fun = "event", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.title = element_blank())
ggsurvplot.cloglog<-ggsurvplot(survfit.t3.gender, fun = "cloglog", conf.int =TRUE)
ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.position = "none")
ggsurv.t3.gender<- list(
ggsurvplot.event,
ggsurvplot.cloglog)
ggsurv.t3.gender <-arrange_ggsurvplots(ggsurv.t3.gender, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t3.gender.png",ggsurv.t3.gender,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t3.gender.png")
at.risk.t3.gender<-ggsurvplot(survfit.t3.gender, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
mutate(date=as.Date("29/02/2020",
"%d/%m/%y")+time) %>%
select(date, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t3.gender,
col.names = c("Date", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Date | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 2020-02-29 | gender=Male | 2,768,246 | 0 | 0 |
| 2020-02-29 | gender=Female | 2,859,274 | 0 | 0 |
| 2020-03-05 | gender=Male | 2,767,555 | 311 | 547 |
| 2020-03-05 | gender=Female | 2,858,576 | 289 | 559 |
| 2020-03-10 | gender=Male | 2,766,634 | 558 | 1,311 |
| 2020-03-10 | gender=Female | 2,857,665 | 511 | 1,384 |
| 2020-03-15 | gender=Male | 2,764,197 | 980 | 3,388 |
| 2020-03-15 | gender=Female | 2,854,793 | 901 | 3,958 |
| 2020-03-20 | gender=Male | 2,756,157 | 1,345 | 12,836 |
| 2020-03-20 | gender=Female | 2,845,411 | 1,255 | 15,155 |
| 2020-03-25 | gender=Male | 2,746,832 | 1,658 | 22,017 |
| 2020-03-25 | gender=Female | 2,834,336 | 1,524 | 26,060 |
| 2020-03-30 | gender=Male | 2,739,460 | 1,934 | 29,204 |
| 2020-03-30 | gender=Female | 2,825,507 | 1,797 | 35,051 |
| 2020-04-04 | gender=Male | 2,731,513 | 2,571 | 34,754 |
| 2020-04-04 | gender=Female | 2,814,463 | 2,422 | 43,231 |
| 2020-04-09 | gender=Male | 2,726,839 | 3,056 | 39,556 |
| 2020-04-09 | gender=Female | 2,807,644 | 2,874 | 50,484 |
| 2020-04-14 | gender=Male | 2,723,689 | 3,198 | 42,624 |
| 2020-04-14 | gender=Female | 2,802,727 | 3,089 | 55,304 |
| 2020-04-19 | gender=Male | 2,718,947 | 3,880 | 45,675 |
| 2020-04-19 | gender=Female | 2,795,479 | 3,872 | 60,365 |
| 2020-04-24 | gender=Male | 2,715,537 | 4,513 | 48,901 |
| 2020-04-24 | gender=Female | 2,790,174 | 4,552 | 65,458 |
| 2020-04-29 | gender=Male | 2,713,248 | 5,052 | 50,790 |
| 2020-04-29 | gender=Female | 2,786,812 | 5,134 | 68,400 |
| 2020-05-04 | gender=Male | 2,711,351 | 5,339 | 52,176 |
| 2020-05-04 | gender=Female | 2,784,464 | 5,399 | 70,131 |
By age
survfit.t3.age_gr<-survfit(Surv(time, status) ~ age_gr,
data = r.healthy.death)
ggsurv.t3.age_gr<- list(
ggsurvplot(survfit.t3.age_gr, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t3.age_gr, fun = "cloglog", conf.int =TRUE))
ggsurv.t3.age_gr <-arrange_ggsurvplots(ggsurv.t3.age_gr, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t3.age_gr.png",ggsurv.t3.age_gr,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t3.age_gr.png")
at.risk.t3.age_gr<-ggsurvplot(survfit.t3.age_gr, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
mutate(date=as.Date("29/02/2020",
"%d/%m/%y")+time) %>%
select(date, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t3.age_gr,
col.names = c("Date", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Date | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 2020-02-29 | age_gr=Under 18 | 967,227 | 0 | 0 |
| 2020-02-29 | age_gr=18 to 39 | 1,438,732 | 0 | 0 |
| 2020-02-29 | age_gr=40 to 59 | 1,788,832 | 0 | 0 |
| 2020-02-29 | age_gr=60 to 69 | 617,929 | 0 | 0 |
| 2020-02-29 | age_gr=70 to 79 | 474,369 | 0 | 0 |
| 2020-02-29 | age_gr=80 or older | 340,431 | 0 | 0 |
| 2020-03-05 | age_gr=Under 18 | 967,148 | 0 | 99 |
| 2020-03-05 | age_gr=18 to 39 | 1,438,396 | 5 | 442 |
| 2020-03-05 | age_gr=40 to 59 | 1,788,516 | 40 | 344 |
| 2020-03-05 | age_gr=60 to 69 | 617,797 | 73 | 97 |
| 2020-03-05 | age_gr=70 to 79 | 474,235 | 102 | 56 |
| 2020-03-05 | age_gr=80 or older | 340,039 | 380 | 68 |
| 2020-03-10 | age_gr=Under 18 | 967,057 | 0 | 183 |
| 2020-03-10 | age_gr=18 to 39 | 1,437,927 | 7 | 952 |
| 2020-03-10 | age_gr=40 to 59 | 1,788,016 | 76 | 948 |
| 2020-03-10 | age_gr=60 to 69 | 617,593 | 126 | 272 |
| 2020-03-10 | age_gr=70 to 79 | 474,071 | 174 | 168 |
| 2020-03-10 | age_gr=80 or older | 339,635 | 686 | 172 |
| 2020-03-15 | age_gr=Under 18 | 966,925 | 2 | 312 |
| 2020-03-15 | age_gr=18 to 39 | 1,436,495 | 15 | 2,385 |
| 2020-03-15 | age_gr=40 to 59 | 1,786,021 | 141 | 2,954 |
| 2020-03-15 | age_gr=60 to 69 | 617,051 | 206 | 757 |
| 2020-03-15 | age_gr=70 to 79 | 473,621 | 328 | 516 |
| 2020-03-15 | age_gr=80 or older | 338,877 | 1,189 | 422 |
| 2020-03-20 | age_gr=Under 18 | 966,550 | 2 | 807 |
| 2020-03-20 | age_gr=18 to 39 | 1,431,489 | 24 | 8,449 |
| 2020-03-20 | age_gr=40 to 59 | 1,778,104 | 190 | 12,436 |
| 2020-03-20 | age_gr=60 to 69 | 615,231 | 274 | 3,000 |
| 2020-03-20 | age_gr=70 to 79 | 472,402 | 437 | 1,939 |
| 2020-03-20 | age_gr=80 or older | 337,792 | 1,673 | 1,360 |
| 2020-03-25 | age_gr=Under 18 | 966,050 | 4 | 1,321 |
| 2020-03-25 | age_gr=18 to 39 | 1,426,062 | 31 | 13,885 |
| 2020-03-25 | age_gr=40 to 59 | 1,769,519 | 226 | 20,977 |
| 2020-03-25 | age_gr=60 to 69 | 612,789 | 333 | 5,382 |
| 2020-03-25 | age_gr=70 to 79 | 470,557 | 533 | 3,749 |
| 2020-03-25 | age_gr=80 or older | 336,191 | 2,055 | 2,763 |
| 2020-03-30 | age_gr=Under 18 | 965,564 | 4 | 1,805 |
| 2020-03-30 | age_gr=18 to 39 | 1,422,131 | 36 | 17,985 |
| 2020-03-30 | age_gr=40 to 59 | 1,763,306 | 257 | 27,616 |
| 2020-03-30 | age_gr=60 to 69 | 610,854 | 386 | 7,278 |
| 2020-03-30 | age_gr=70 to 79 | 468,883 | 627 | 5,225 |
| 2020-03-30 | age_gr=80 or older | 334,229 | 2,421 | 4,346 |
| 2020-04-04 | age_gr=Under 18 | 964,820 | 5 | 2,420 |
| 2020-04-04 | age_gr=18 to 39 | 1,417,358 | 45 | 21,612 |
| 2020-04-04 | age_gr=40 to 59 | 1,756,123 | 328 | 32,776 |
| 2020-04-04 | age_gr=60 to 69 | 608,859 | 498 | 8,718 |
| 2020-04-04 | age_gr=70 to 79 | 467,358 | 847 | 6,344 |
| 2020-04-04 | age_gr=80 or older | 331,458 | 3,270 | 6,115 |
| 2020-04-09 | age_gr=Under 18 | 964,348 | 5 | 3,027 |
| 2020-04-09 | age_gr=18 to 39 | 1,414,606 | 50 | 24,705 |
| 2020-04-09 | age_gr=40 to 59 | 1,752,126 | 375 | 37,200 |
| 2020-04-09 | age_gr=60 to 69 | 607,762 | 583 | 9,860 |
| 2020-04-09 | age_gr=70 to 79 | 466,393 | 1,023 | 7,211 |
| 2020-04-09 | age_gr=80 or older | 329,248 | 3,894 | 8,037 |
| 2020-04-14 | age_gr=Under 18 | 963,998 | 5 | 3,382 |
| 2020-04-14 | age_gr=18 to 39 | 1,412,804 | 50 | 26,650 |
| 2020-04-14 | age_gr=40 to 59 | 1,749,659 | 398 | 39,888 |
| 2020-04-14 | age_gr=60 to 69 | 607,070 | 608 | 10,523 |
| 2020-04-14 | age_gr=70 to 79 | 465,724 | 1,089 | 7,784 |
| 2020-04-14 | age_gr=80 or older | 327,161 | 4,137 | 9,701 |
| 2020-04-19 | age_gr=Under 18 | 963,456 | 6 | 3,791 |
| 2020-04-19 | age_gr=18 to 39 | 1,410,279 | 55 | 28,526 |
| 2020-04-19 | age_gr=40 to 59 | 1,746,184 | 460 | 42,349 |
| 2020-04-19 | age_gr=60 to 69 | 606,044 | 711 | 11,225 |
| 2020-04-19 | age_gr=70 to 79 | 464,575 | 1,353 | 8,506 |
| 2020-04-19 | age_gr=80 or older | 323,888 | 5,167 | 11,643 |
| 2020-04-24 | age_gr=Under 18 | 963,003 | 7 | 4,298 |
| 2020-04-24 | age_gr=18 to 39 | 1,408,187 | 63 | 30,854 |
| 2020-04-24 | age_gr=40 to 59 | 1,743,638 | 515 | 45,160 |
| 2020-04-24 | age_gr=60 to 69 | 605,380 | 824 | 11,894 |
| 2020-04-24 | age_gr=70 to 79 | 463,869 | 1,557 | 9,092 |
| 2020-04-24 | age_gr=80 or older | 321,634 | 6,099 | 13,061 |
| 2020-04-29 | age_gr=Under 18 | 962,652 | 7 | 4,665 |
| 2020-04-29 | age_gr=18 to 39 | 1,406,826 | 74 | 32,184 |
| 2020-04-29 | age_gr=40 to 59 | 1,741,926 | 582 | 46,814 |
| 2020-04-29 | age_gr=60 to 69 | 604,888 | 927 | 12,288 |
| 2020-04-29 | age_gr=70 to 79 | 463,397 | 1,762 | 9,401 |
| 2020-04-29 | age_gr=80 or older | 320,371 | 6,834 | 13,838 |
| 2020-05-04 | age_gr=Under 18 | 962,391 | 9 | 4,930 |
| 2020-05-04 | age_gr=18 to 39 | 1,405,894 | 75 | 33,108 |
| 2020-05-04 | age_gr=40 to 59 | 1,740,729 | 613 | 47,939 |
| 2020-05-04 | age_gr=60 to 69 | 604,521 | 975 | 12,554 |
| 2020-05-04 | age_gr=70 to 79 | 463,006 | 1,865 | 9,591 |
| 2020-05-04 | age_gr=80 or older | 319,274 | 7,201 | 14,185 |
By charlson
survfit.t3.charlson<-survfit(Surv(time, status) ~ charlson,
data = r.healthy.death)
ggsurv.t3.charlson<- list(
ggsurvplot(survfit.t3.charlson, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t3.charlson, fun = "cloglog", conf.int =TRUE))
ggsurv.t3.charlson <-arrange_ggsurvplots(ggsurv.t3.charlson, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t3.charlson.png",ggsurv.t3.charlson,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t3.charlson.png")
at.risk.t3.charlson<-ggsurvplot(survfit.t3.charlson, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
mutate(date=as.Date("29/02/2020",
"%d/%m/%y")+time) %>%
select(date, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t3.charlson,
col.names = c("Date", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Date | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 2020-02-29 | charlson=0 | 4,572,265 | 0 | 0 |
| 2020-02-29 | charlson=1 | 410,497 | 0 | 0 |
| 2020-02-29 | charlson=2 | 363,057 | 0 | 0 |
| 2020-02-29 | charlson=3+ | 281,701 | 0 | 0 |
| 2020-03-05 | charlson=0 | 4,571,509 | 69 | 897 |
| 2020-03-05 | charlson=1 | 410,364 | 82 | 80 |
| 2020-03-05 | charlson=2 | 362,897 | 116 | 73 |
| 2020-03-05 | charlson=3+ | 281,361 | 333 | 56 |
| 2020-03-10 | charlson=0 | 4,570,392 | 135 | 2,126 |
| 2020-03-10 | charlson=1 | 410,196 | 134 | 219 |
| 2020-03-10 | charlson=2 | 362,693 | 217 | 185 |
| 2020-03-10 | charlson=3+ | 281,018 | 583 | 165 |
| 2020-03-15 | charlson=0 | 4,566,799 | 225 | 5,732 |
| 2020-03-15 | charlson=1 | 409,735 | 225 | 618 |
| 2020-03-15 | charlson=2 | 362,184 | 380 | 549 |
| 2020-03-15 | charlson=3+ | 280,272 | 1,051 | 447 |
| 2020-03-20 | charlson=0 | 4,553,390 | 322 | 22,005 |
| 2020-03-20 | charlson=1 | 408,137 | 313 | 2,457 |
| 2020-03-20 | charlson=2 | 360,932 | 522 | 1,974 |
| 2020-03-20 | charlson=3+ | 279,109 | 1,443 | 1,555 |
| 2020-03-25 | charlson=0 | 4,538,019 | 399 | 37,341 |
| 2020-03-25 | charlson=1 | 406,294 | 403 | 4,285 |
| 2020-03-25 | charlson=2 | 359,276 | 639 | 3,549 |
| 2020-03-25 | charlson=3+ | 277,579 | 1,741 | 2,902 |
| 2020-03-30 | charlson=0 | 4,526,633 | 460 | 49,211 |
| 2020-03-30 | charlson=1 | 404,704 | 472 | 5,809 |
| 2020-03-30 | charlson=2 | 357,781 | 760 | 4,933 |
| 2020-03-30 | charlson=3+ | 275,849 | 2,039 | 4,302 |
| 2020-04-04 | charlson=0 | 4,513,349 | 655 | 59,061 |
| 2020-04-04 | charlson=1 | 402,809 | 659 | 7,184 |
| 2020-04-04 | charlson=2 | 356,155 | 1,010 | 6,062 |
| 2020-04-04 | charlson=3+ | 273,663 | 2,669 | 5,678 |
| 2020-04-09 | charlson=0 | 4,505,766 | 805 | 67,487 |
| 2020-04-09 | charlson=1 | 401,582 | 794 | 8,451 |
| 2020-04-09 | charlson=2 | 355,075 | 1,213 | 7,097 |
| 2020-04-09 | charlson=3+ | 272,060 | 3,118 | 7,005 |
| 2020-04-14 | charlson=0 | 4,500,816 | 845 | 72,706 |
| 2020-04-14 | charlson=1 | 400,626 | 856 | 9,364 |
| 2020-04-14 | charlson=2 | 354,220 | 1,290 | 7,817 |
| 2020-04-14 | charlson=3+ | 270,754 | 3,296 | 8,041 |
| 2020-04-19 | charlson=0 | 4,493,753 | 1,064 | 77,815 |
| 2020-04-19 | charlson=1 | 399,158 | 1,073 | 10,354 |
| 2020-04-19 | charlson=2 | 352,951 | 1,573 | 8,623 |
| 2020-04-19 | charlson=3+ | 268,564 | 4,042 | 9,248 |
| 2020-04-24 | charlson=0 | 4,488,361 | 1,243 | 83,654 |
| 2020-04-24 | charlson=1 | 398,135 | 1,288 | 11,248 |
| 2020-04-24 | charlson=2 | 352,068 | 1,860 | 9,300 |
| 2020-04-24 | charlson=3+ | 267,147 | 4,674 | 10,157 |
| 2020-04-29 | charlson=0 | 4,484,759 | 1,422 | 87,088 |
| 2020-04-29 | charlson=1 | 397,529 | 1,467 | 11,748 |
| 2020-04-29 | charlson=2 | 351,520 | 2,089 | 9,695 |
| 2020-04-29 | charlson=3+ | 266,252 | 5,208 | 10,659 |
| 2020-05-04 | charlson=0 | 4,482,261 | 1,504 | 89,428 |
| 2020-05-04 | charlson=1 | 397,044 | 1,535 | 12,051 |
| 2020-05-04 | charlson=2 | 351,026 | 2,224 | 9,921 |
| 2020-05-04 | charlson=3+ | 265,484 | 5,475 | 10,907 |
By a_autoimmune_condition
survfit.t3.a_autoimmune_condition<-survfit(Surv(time, status) ~ a_autoimmune_condition,
data = r.healthy.death)
ggsurv.t3.a_autoimmune_condition<- list(
ggsurvplot(survfit.t3.a_autoimmune_condition, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t3.a_autoimmune_condition, fun = "cloglog", conf.int =TRUE))
ggsurv.t3.a_autoimmune_condition <-arrange_ggsurvplots(ggsurv.t3.a_autoimmune_condition, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t3.a_autoimmune_condition.png",ggsurv.t3.a_autoimmune_condition,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t3.a_autoimmune_condition.png")
at.risk.t3.a_autoimmune_condition<-ggsurvplot(survfit.t3.a_autoimmune_condition, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
mutate(date=as.Date("29/02/2020",
"%d/%m/%y")+time) %>%
select(date, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t3.a_autoimmune_condition,
col.names = c("Date", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Date | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 2020-02-29 | a_autoimmune_condition=0 | 5,350,070 | 0 | 0 |
| 2020-02-29 | a_autoimmune_condition=1 | 277,450 | 0 | 0 |
| 2020-03-05 | a_autoimmune_condition=0 | 5,348,777 | 527 | 1,059 |
| 2020-03-05 | a_autoimmune_condition=1 | 277,354 | 73 | 47 |
| 2020-03-10 | a_autoimmune_condition=0 | 5,347,098 | 931 | 2,552 |
| 2020-03-10 | a_autoimmune_condition=1 | 277,201 | 138 | 143 |
| 2020-03-15 | a_autoimmune_condition=0 | 5,342,174 | 1,639 | 6,903 |
| 2020-03-15 | a_autoimmune_condition=1 | 276,816 | 242 | 443 |
| 2020-03-20 | a_autoimmune_condition=0 | 5,325,892 | 2,280 | 26,239 |
| 2020-03-20 | a_autoimmune_condition=1 | 275,676 | 320 | 1,752 |
| 2020-03-25 | a_autoimmune_condition=0 | 5,306,825 | 2,808 | 45,039 |
| 2020-03-25 | a_autoimmune_condition=1 | 274,343 | 374 | 3,038 |
| 2020-03-30 | a_autoimmune_condition=0 | 5,291,709 | 3,276 | 60,169 |
| 2020-03-30 | a_autoimmune_condition=1 | 273,258 | 455 | 4,086 |
| 2020-04-04 | a_autoimmune_condition=0 | 5,274,026 | 4,402 | 72,981 |
| 2020-04-04 | a_autoimmune_condition=1 | 271,950 | 591 | 5,004 |
| 2020-04-09 | a_autoimmune_condition=0 | 5,263,311 | 5,228 | 84,235 |
| 2020-04-09 | a_autoimmune_condition=1 | 271,172 | 702 | 5,805 |
| 2020-04-14 | a_autoimmune_condition=0 | 5,255,822 | 5,544 | 91,589 |
| 2020-04-14 | a_autoimmune_condition=1 | 270,594 | 743 | 6,339 |
| 2020-04-19 | a_autoimmune_condition=0 | 5,244,775 | 6,851 | 99,090 |
| 2020-04-19 | a_autoimmune_condition=1 | 269,651 | 901 | 6,950 |
| 2020-04-24 | a_autoimmune_condition=0 | 5,236,645 | 8,029 | 106,911 |
| 2020-04-24 | a_autoimmune_condition=1 | 269,066 | 1,036 | 7,448 |
| 2020-04-29 | a_autoimmune_condition=0 | 5,231,350 | 9,031 | 111,462 |
| 2020-04-29 | a_autoimmune_condition=1 | 268,710 | 1,155 | 7,728 |
| 2020-05-04 | a_autoimmune_condition=0 | 5,227,423 | 9,513 | 114,395 |
| 2020-05-04 | a_autoimmune_condition=1 | 268,392 | 1,225 | 7,912 |
By a_chronic_kidney_disease
survfit.t3.a_chronic_kidney_disease<-survfit(Surv(time, status) ~ a_chronic_kidney_disease,
data = r.healthy.death)
ggsurv.t3.a_chronic_kidney_disease<- list(
ggsurvplot(survfit.t3.a_chronic_kidney_disease, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t3.a_chronic_kidney_disease, fun = "cloglog", conf.int =TRUE))
ggsurv.t3.a_chronic_kidney_disease <-arrange_ggsurvplots(ggsurv.t3.a_chronic_kidney_disease, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t3.a_chronic_kidney_disease.png",ggsurv.t3.a_chronic_kidney_disease,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t3.a_chronic_kidney_disease.png")
at.risk.t3.a_chronic_kidney_disease<-ggsurvplot(survfit.t3.a_chronic_kidney_disease, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
mutate(date=as.Date("29/02/2020",
"%d/%m/%y")+time) %>%
select(date, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t3.a_chronic_kidney_disease,
col.names = c("Date", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Date | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 2020-02-29 | a_chronic_kidney_disease=0 | 5,415,678 | 0 | 0 |
| 2020-02-29 | a_chronic_kidney_disease=1 | 211,842 | 0 | 0 |
| 2020-03-05 | a_chronic_kidney_disease=0 | 5,414,480 | 408 | 1,073 |
| 2020-03-05 | a_chronic_kidney_disease=1 | 211,651 | 192 | 33 |
| 2020-03-10 | a_chronic_kidney_disease=0 | 5,412,861 | 725 | 2,599 |
| 2020-03-10 | a_chronic_kidney_disease=1 | 211,438 | 344 | 96 |
| 2020-03-15 | a_chronic_kidney_disease=0 | 5,407,958 | 1,289 | 7,078 |
| 2020-03-15 | a_chronic_kidney_disease=1 | 211,032 | 592 | 268 |
| 2020-03-20 | a_chronic_kidney_disease=0 | 5,391,344 | 1,749 | 26,949 |
| 2020-03-20 | a_chronic_kidney_disease=1 | 210,224 | 851 | 1,042 |
| 2020-03-25 | a_chronic_kidney_disease=0 | 5,372,036 | 2,132 | 46,073 |
| 2020-03-25 | a_chronic_kidney_disease=1 | 209,132 | 1,050 | 2,004 |
| 2020-03-30 | a_chronic_kidney_disease=0 | 5,357,039 | 2,470 | 61,253 |
| 2020-03-30 | a_chronic_kidney_disease=1 | 207,928 | 1,261 | 3,002 |
| 2020-04-04 | a_chronic_kidney_disease=0 | 5,339,574 | 3,309 | 74,024 |
| 2020-04-04 | a_chronic_kidney_disease=1 | 206,402 | 1,684 | 3,961 |
| 2020-04-09 | a_chronic_kidney_disease=0 | 5,329,185 | 3,924 | 85,141 |
| 2020-04-09 | a_chronic_kidney_disease=1 | 205,298 | 2,006 | 4,899 |
| 2020-04-14 | a_chronic_kidney_disease=0 | 5,322,027 | 4,157 | 92,348 |
| 2020-04-14 | a_chronic_kidney_disease=1 | 204,389 | 2,130 | 5,580 |
| 2020-04-19 | a_chronic_kidney_disease=0 | 5,311,447 | 5,139 | 99,681 |
| 2020-04-19 | a_chronic_kidney_disease=1 | 202,979 | 2,613 | 6,359 |
| 2020-04-24 | a_chronic_kidney_disease=0 | 5,303,686 | 6,028 | 107,395 |
| 2020-04-24 | a_chronic_kidney_disease=1 | 202,025 | 3,037 | 6,964 |
| 2020-04-29 | a_chronic_kidney_disease=0 | 5,298,621 | 6,806 | 111,889 |
| 2020-04-29 | a_chronic_kidney_disease=1 | 201,439 | 3,380 | 7,301 |
| 2020-05-04 | a_chronic_kidney_disease=0 | 5,294,888 | 7,173 | 114,850 |
| 2020-05-04 | a_chronic_kidney_disease=1 | 200,927 | 3,565 | 7,457 |
By a_copd
survfit.t3.a_copd<-survfit(Surv(time, status) ~ a_copd,
data = r.healthy.death)
ggsurv.t3.a_copd<- list(
ggsurvplot(survfit.t3.a_copd, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t3.a_copd, fun = "cloglog", conf.int =TRUE))
ggsurv.t3.a_copd <-arrange_ggsurvplots(ggsurv.t3.a_copd, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t3.a_copd.png",ggsurv.t3.a_copd,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t3.a_copd.png")
at.risk.t3.a_copd<-ggsurvplot(survfit.t3.a_copd, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
mutate(date=as.Date("29/02/2020",
"%d/%m/%y")+time) %>%
select(date, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t3.a_copd,
col.names = c("Date", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Date | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 2020-02-29 | a_copd=0 | 5,504,775 | 0 | 0 |
| 2020-02-29 | a_copd=1 | 122,745 | 0 | 0 |
| 2020-03-05 | a_copd=0 | 5,503,475 | 518 | 1,082 |
| 2020-03-05 | a_copd=1 | 122,656 | 82 | 24 |
| 2020-03-10 | a_copd=0 | 5,501,747 | 926 | 2,624 |
| 2020-03-10 | a_copd=1 | 122,552 | 143 | 71 |
| 2020-03-15 | a_copd=0 | 5,496,698 | 1,607 | 7,142 |
| 2020-03-15 | a_copd=1 | 122,292 | 274 | 204 |
| 2020-03-20 | a_copd=0 | 5,479,797 | 2,218 | 27,271 |
| 2020-03-20 | a_copd=1 | 121,771 | 382 | 720 |
| 2020-03-25 | a_copd=0 | 5,459,959 | 2,722 | 46,834 |
| 2020-03-25 | a_copd=1 | 121,209 | 460 | 1,243 |
| 2020-03-30 | a_copd=0 | 5,444,340 | 3,200 | 62,519 |
| 2020-03-30 | a_copd=1 | 120,627 | 531 | 1,736 |
| 2020-04-04 | a_copd=0 | 5,426,001 | 4,294 | 75,822 |
| 2020-04-04 | a_copd=1 | 119,975 | 699 | 2,163 |
| 2020-04-09 | a_copd=0 | 5,414,970 | 5,111 | 87,497 |
| 2020-04-09 | a_copd=1 | 119,513 | 819 | 2,543 |
| 2020-04-14 | a_copd=0 | 5,407,253 | 5,430 | 95,084 |
| 2020-04-14 | a_copd=1 | 119,163 | 857 | 2,844 |
| 2020-04-19 | a_copd=0 | 5,395,840 | 6,725 | 102,861 |
| 2020-04-19 | a_copd=1 | 118,586 | 1,027 | 3,179 |
| 2020-04-24 | a_copd=0 | 5,387,537 | 7,888 | 110,891 |
| 2020-04-24 | a_copd=1 | 118,174 | 1,177 | 3,468 |
| 2020-04-29 | a_copd=0 | 5,382,126 | 8,878 | 115,570 |
| 2020-04-29 | a_copd=1 | 117,934 | 1,308 | 3,620 |
| 2020-05-04 | a_copd=0 | 5,378,108 | 9,347 | 118,603 |
| 2020-05-04 | a_copd=1 | 117,707 | 1,391 | 3,704 |
By a_dementia
survfit.t3.a_dementia<-survfit(Surv(time, status) ~ a_dementia,
data = r.healthy.death)
ggsurv.t3.a_dementia<- list(
ggsurvplot(survfit.t3.a_dementia, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t3.a_dementia, fun = "cloglog", conf.int =TRUE))
ggsurv.t3.a_dementia <-arrange_ggsurvplots(ggsurv.t3.a_dementia, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t3.a_dementia.png",ggsurv.t3.a_dementia,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t3.a_dementia.png")
at.risk.t3.a_dementia<-ggsurvplot(survfit.t3.a_dementia, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
mutate(date=as.Date("29/02/2020",
"%d/%m/%y")+time) %>%
select(date, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t3.a_dementia,
col.names = c("Date", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Date | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 2020-02-29 | a_dementia=0 | 5,570,210 | 0 | 0 |
| 2020-02-29 | a_dementia=1 | 57,310 | 0 | 0 |
| 2020-03-05 | a_dementia=0 | 5,568,939 | 486 | 1,088 |
| 2020-03-05 | a_dementia=1 | 57,192 | 114 | 18 |
| 2020-03-10 | a_dementia=0 | 5,567,221 | 859 | 2,655 |
| 2020-03-10 | a_dementia=1 | 57,078 | 210 | 40 |
| 2020-03-15 | a_dementia=0 | 5,562,101 | 1,531 | 7,263 |
| 2020-03-15 | a_dementia=1 | 56,889 | 350 | 83 |
| 2020-03-20 | a_dementia=0 | 5,544,920 | 2,117 | 27,703 |
| 2020-03-20 | a_dementia=1 | 56,648 | 483 | 288 |
| 2020-03-25 | a_dementia=0 | 5,524,879 | 2,602 | 47,465 |
| 2020-03-25 | a_dementia=1 | 56,289 | 580 | 612 |
| 2020-03-30 | a_dementia=0 | 5,509,252 | 3,054 | 63,144 |
| 2020-03-30 | a_dementia=1 | 55,715 | 677 | 1,111 |
| 2020-04-04 | a_dementia=0 | 5,491,324 | 4,026 | 76,161 |
| 2020-04-04 | a_dementia=1 | 54,652 | 967 | 1,824 |
| 2020-04-09 | a_dementia=0 | 5,480,691 | 4,735 | 87,408 |
| 2020-04-09 | a_dementia=1 | 53,792 | 1,195 | 2,632 |
| 2020-04-14 | a_dementia=0 | 5,473,567 | 4,973 | 94,517 |
| 2020-04-14 | a_dementia=1 | 52,849 | 1,314 | 3,411 |
| 2020-04-19 | a_dementia=0 | 5,463,121 | 5,997 | 101,679 |
| 2020-04-19 | a_dementia=1 | 51,305 | 1,755 | 4,361 |
| 2020-04-24 | a_dementia=0 | 5,455,345 | 6,919 | 109,422 |
| 2020-04-24 | a_dementia=1 | 50,366 | 2,146 | 4,937 |
| 2020-04-29 | a_dementia=0 | 5,450,193 | 7,758 | 113,934 |
| 2020-04-29 | a_dementia=1 | 49,867 | 2,428 | 5,256 |
| 2020-05-04 | a_dementia=0 | 5,446,370 | 8,175 | 116,910 |
| 2020-05-04 | a_dementia=1 | 49,445 | 2,563 | 5,397 |
By a_heart_disease
survfit.t3.a_heart_disease<-survfit(Surv(time, status) ~ a_heart_disease,
data = r.healthy.death)
ggsurv.t3.a_heart_disease<- list(
ggsurvplot(survfit.t3.a_heart_disease, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t3.a_heart_disease, fun = "cloglog", conf.int =TRUE))
ggsurv.t3.a_heart_disease <-arrange_ggsurvplots(ggsurv.t3.a_heart_disease, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t3.a_heart_disease.png",ggsurv.t3.a_heart_disease,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t3.a_heart_disease.png")
at.risk.t3.a_heart_disease<-ggsurvplot(survfit.t3.a_heart_disease, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
mutate(date=as.Date("29/02/2020",
"%d/%m/%y")+time) %>%
select(date, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t3.a_heart_disease,
col.names = c("Date", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Date | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 2020-02-29 | a_heart_disease=0 | 5,079,725 | 0 | 0 |
| 2020-02-29 | a_heart_disease=1 | 547,795 | 0 | 0 |
| 2020-03-05 | a_heart_disease=0 | 5,078,692 | 281 | 1,009 |
| 2020-03-05 | a_heart_disease=1 | 547,439 | 319 | 97 |
| 2020-03-10 | a_heart_disease=0 | 5,077,250 | 510 | 2,421 |
| 2020-03-10 | a_heart_disease=1 | 547,049 | 559 | 274 |
| 2020-03-15 | a_heart_disease=0 | 5,072,893 | 872 | 6,540 |
| 2020-03-15 | a_heart_disease=1 | 546,097 | 1,009 | 806 |
| 2020-03-20 | a_heart_disease=0 | 5,057,540 | 1,222 | 24,998 |
| 2020-03-20 | a_heart_disease=1 | 544,028 | 1,378 | 2,993 |
| 2020-03-25 | a_heart_disease=0 | 5,039,789 | 1,490 | 42,636 |
| 2020-03-25 | a_heart_disease=1 | 541,379 | 1,692 | 5,441 |
| 2020-03-30 | a_heart_disease=0 | 5,026,102 | 1,746 | 56,604 |
| 2020-03-30 | a_heart_disease=1 | 538,865 | 1,985 | 7,651 |
| 2020-04-04 | a_heart_disease=0 | 5,010,132 | 2,368 | 68,307 |
| 2020-04-04 | a_heart_disease=1 | 535,844 | 2,625 | 9,678 |
| 2020-04-09 | a_heart_disease=0 | 5,000,662 | 2,847 | 78,555 |
| 2020-04-09 | a_heart_disease=1 | 533,821 | 3,083 | 11,485 |
| 2020-04-14 | a_heart_disease=0 | 4,994,212 | 3,035 | 85,103 |
| 2020-04-14 | a_heart_disease=1 | 532,204 | 3,252 | 12,825 |
| 2020-04-19 | a_heart_disease=0 | 4,984,742 | 3,775 | 91,733 |
| 2020-04-19 | a_heart_disease=1 | 529,684 | 3,977 | 14,307 |
| 2020-04-24 | a_heart_disease=0 | 4,977,727 | 4,462 | 98,827 |
| 2020-04-24 | a_heart_disease=1 | 527,984 | 4,603 | 15,532 |
| 2020-04-29 | a_heart_disease=0 | 4,973,141 | 5,053 | 102,949 |
| 2020-04-29 | a_heart_disease=1 | 526,919 | 5,133 | 16,241 |
| 2020-05-04 | a_heart_disease=0 | 4,969,819 | 5,335 | 105,710 |
| 2020-05-04 | a_heart_disease=1 | 525,996 | 5,403 | 16,597 |
By a_hyperlipidemia
survfit.t3.a_hyperlipidemia<-survfit(Surv(time, status) ~ a_hyperlipidemia,
data = r.healthy.death)
ggsurv.t3.a_hyperlipidemia<- list(
ggsurvplot(survfit.t3.a_hyperlipidemia, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t3.a_hyperlipidemia, fun = "cloglog", conf.int =TRUE))
ggsurv.t3.a_hyperlipidemia <-arrange_ggsurvplots(ggsurv.t3.a_hyperlipidemia, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t3.a_hyperlipidemia.png",ggsurv.t3.a_hyperlipidemia,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t3.a_hyperlipidemia.png")
at.risk.t3.a_hyperlipidemia<-ggsurvplot(survfit.t3.a_hyperlipidemia, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
mutate(date=as.Date("29/02/2020",
"%d/%m/%y")+time) %>%
select(date, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t3.a_hyperlipidemia,
col.names = c("Date", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Date | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 2020-02-29 | a_hyperlipidemia=0 | 5,106,683 | 0 | 0 |
| 2020-02-29 | a_hyperlipidemia=1 | 520,837 | 0 | 0 |
| 2020-03-05 | a_hyperlipidemia=0 | 5,105,416 | 529 | 1,029 |
| 2020-03-05 | a_hyperlipidemia=1 | 520,715 | 71 | 77 |
| 2020-03-10 | a_hyperlipidemia=0 | 5,103,770 | 931 | 2,479 |
| 2020-03-10 | a_hyperlipidemia=1 | 520,529 | 138 | 216 |
| 2020-03-15 | a_hyperlipidemia=0 | 5,098,984 | 1,637 | 6,661 |
| 2020-03-15 | a_hyperlipidemia=1 | 520,006 | 244 | 685 |
| 2020-03-20 | a_hyperlipidemia=0 | 5,083,527 | 2,259 | 25,007 |
| 2020-03-20 | a_hyperlipidemia=1 | 518,041 | 341 | 2,984 |
| 2020-03-25 | a_hyperlipidemia=0 | 5,065,469 | 2,749 | 42,792 |
| 2020-03-25 | a_hyperlipidemia=1 | 515,699 | 433 | 5,285 |
| 2020-03-30 | a_hyperlipidemia=0 | 5,051,255 | 3,216 | 57,017 |
| 2020-03-30 | a_hyperlipidemia=1 | 513,712 | 515 | 7,238 |
| 2020-04-04 | a_hyperlipidemia=0 | 5,034,476 | 4,315 | 69,157 |
| 2020-04-04 | a_hyperlipidemia=1 | 511,500 | 678 | 8,828 |
| 2020-04-09 | a_hyperlipidemia=0 | 5,024,309 | 5,121 | 79,853 |
| 2020-04-09 | a_hyperlipidemia=1 | 510,174 | 809 | 10,187 |
| 2020-04-14 | a_hyperlipidemia=0 | 5,017,120 | 5,431 | 86,899 |
| 2020-04-14 | a_hyperlipidemia=1 | 509,296 | 856 | 11,029 |
| 2020-04-19 | a_hyperlipidemia=0 | 5,006,528 | 6,686 | 94,079 |
| 2020-04-19 | a_hyperlipidemia=1 | 507,898 | 1,066 | 11,961 |
| 2020-04-24 | a_hyperlipidemia=0 | 4,998,802 | 7,834 | 101,494 |
| 2020-04-24 | a_hyperlipidemia=1 | 506,909 | 1,231 | 12,865 |
| 2020-04-29 | a_hyperlipidemia=0 | 4,993,771 | 8,781 | 105,814 |
| 2020-04-29 | a_hyperlipidemia=1 | 506,289 | 1,405 | 13,376 |
| 2020-05-04 | a_hyperlipidemia=0 | 4,989,987 | 9,266 | 108,626 |
| 2020-05-04 | a_hyperlipidemia=1 | 505,828 | 1,472 | 13,681 |
By a_hypertension
survfit.t3.a_hypertension<-survfit(Surv(time, status) ~ a_hypertension,
data = r.healthy.death)
ggsurv.t3.a_hypertension<- list(
ggsurvplot(survfit.t3.a_hypertension, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t3.a_hypertension, fun = "cloglog", conf.int =TRUE))
ggsurv.t3.a_hypertension <-arrange_ggsurvplots(ggsurv.t3.a_hypertension, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t3.a_hypertension.png",ggsurv.t3.a_hypertension,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t3.a_hypertension.png")
at.risk.t3.a_hypertension<-ggsurvplot(survfit.t3.a_hypertension, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
mutate(date=as.Date("29/02/2020",
"%d/%m/%y")+time) %>%
select(date, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t3.a_hypertension,
col.names = c("Date", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Date | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 2020-02-29 | a_hypertension=0 | 4,924,353 | 0 | 0 |
| 2020-02-29 | a_hypertension=1 | 703,167 | 0 | 0 |
| 2020-03-05 | a_hypertension=0 | 4,923,233 | 387 | 1,004 |
| 2020-03-05 | a_hypertension=1 | 702,898 | 213 | 102 |
| 2020-03-10 | a_hypertension=0 | 4,921,708 | 708 | 2,385 |
| 2020-03-10 | a_hypertension=1 | 702,591 | 361 | 310 |
| 2020-03-15 | a_hypertension=0 | 4,917,288 | 1,257 | 6,369 |
| 2020-03-15 | a_hypertension=1 | 701,702 | 624 | 977 |
| 2020-03-20 | a_hypertension=0 | 4,902,358 | 1,732 | 24,167 |
| 2020-03-20 | a_hypertension=1 | 699,210 | 868 | 3,824 |
| 2020-03-25 | a_hypertension=0 | 4,885,164 | 2,128 | 41,134 |
| 2020-03-25 | a_hypertension=1 | 696,004 | 1,054 | 6,943 |
| 2020-03-30 | a_hypertension=0 | 4,871,704 | 2,480 | 54,764 |
| 2020-03-30 | a_hypertension=1 | 693,263 | 1,251 | 9,491 |
| 2020-04-04 | a_hypertension=0 | 4,855,829 | 3,296 | 66,337 |
| 2020-04-04 | a_hypertension=1 | 690,147 | 1,697 | 11,648 |
| 2020-04-09 | a_hypertension=0 | 4,846,380 | 3,919 | 76,428 |
| 2020-04-09 | a_hypertension=1 | 688,103 | 2,011 | 13,612 |
| 2020-04-14 | a_hypertension=0 | 4,839,876 | 4,165 | 82,859 |
| 2020-04-14 | a_hypertension=1 | 686,540 | 2,122 | 15,069 |
| 2020-04-19 | a_hypertension=0 | 4,830,390 | 5,169 | 89,330 |
| 2020-04-19 | a_hypertension=1 | 684,036 | 2,583 | 16,710 |
| 2020-04-24 | a_hypertension=0 | 4,823,371 | 6,021 | 96,279 |
| 2020-04-24 | a_hypertension=1 | 682,340 | 3,044 | 18,080 |
| 2020-04-29 | a_hypertension=0 | 4,818,708 | 6,780 | 100,379 |
| 2020-04-29 | a_hypertension=1 | 681,352 | 3,406 | 18,811 |
| 2020-05-04 | a_hypertension=0 | 4,815,278 | 7,140 | 103,039 |
| 2020-05-04 | a_hypertension=1 | 680,537 | 3,598 | 19,268 |
By a_malignant_neoplasm
survfit.t3.a_malignant_neoplasm<-survfit(Surv(time, status) ~ a_malignant_neoplasm,
data = r.healthy.death)
ggsurv.t3.a_malignant_neoplasm<- list(
ggsurvplot(survfit.t3.a_malignant_neoplasm, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t3.a_malignant_neoplasm, fun = "cloglog", conf.int =TRUE))
ggsurv.t3.a_malignant_neoplasm <-arrange_ggsurvplots(ggsurv.t3.a_malignant_neoplasm, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t3.a_malignant_neoplasm.png",ggsurv.t3.a_malignant_neoplasm,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t3.a_malignant_neoplasm.png")
at.risk.t3.a_malignant_neoplasm<-ggsurvplot(survfit.t3.a_malignant_neoplasm, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
mutate(date=as.Date("29/02/2020",
"%d/%m/%y")+time) %>%
select(date, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t3.a_malignant_neoplasm,
col.names = c("Date", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Date | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 2020-02-29 | a_malignant_neoplasm=0 | 5,330,111 | 0 | 0 |
| 2020-02-29 | a_malignant_neoplasm=1 | 297,409 | 0 | 0 |
| 2020-03-05 | a_malignant_neoplasm=0 | 5,328,988 | 357 | 1,046 |
| 2020-03-05 | a_malignant_neoplasm=1 | 297,143 | 243 | 60 |
| 2020-03-10 | a_malignant_neoplasm=0 | 5,327,436 | 629 | 2,539 |
| 2020-03-10 | a_malignant_neoplasm=1 | 296,863 | 440 | 156 |
| 2020-03-15 | a_malignant_neoplasm=0 | 5,322,764 | 1,111 | 6,884 |
| 2020-03-15 | a_malignant_neoplasm=1 | 296,226 | 770 | 462 |
| 2020-03-20 | a_malignant_neoplasm=0 | 5,306,486 | 1,561 | 26,340 |
| 2020-03-20 | a_malignant_neoplasm=1 | 295,082 | 1,039 | 1,651 |
| 2020-03-25 | a_malignant_neoplasm=0 | 5,287,532 | 1,937 | 45,124 |
| 2020-03-25 | a_malignant_neoplasm=1 | 293,636 | 1,245 | 2,953 |
| 2020-03-30 | a_malignant_neoplasm=0 | 5,272,714 | 2,292 | 60,191 |
| 2020-03-30 | a_malignant_neoplasm=1 | 292,253 | 1,439 | 4,064 |
| 2020-04-04 | a_malignant_neoplasm=0 | 5,255,239 | 3,155 | 72,942 |
| 2020-04-04 | a_malignant_neoplasm=1 | 290,737 | 1,838 | 5,043 |
| 2020-04-09 | a_malignant_neoplasm=0 | 5,244,805 | 3,822 | 84,135 |
| 2020-04-09 | a_malignant_neoplasm=1 | 289,678 | 2,108 | 5,905 |
| 2020-04-14 | a_malignant_neoplasm=0 | 5,237,487 | 4,070 | 91,432 |
| 2020-04-14 | a_malignant_neoplasm=1 | 288,929 | 2,217 | 6,496 |
| 2020-04-19 | a_malignant_neoplasm=0 | 5,226,756 | 5,111 | 98,868 |
| 2020-04-19 | a_malignant_neoplasm=1 | 287,670 | 2,641 | 7,172 |
| 2020-04-24 | a_malignant_neoplasm=0 | 5,218,863 | 6,050 | 106,631 |
| 2020-04-24 | a_malignant_neoplasm=1 | 286,848 | 3,015 | 7,728 |
| 2020-04-29 | a_malignant_neoplasm=0 | 5,213,776 | 6,814 | 111,151 |
| 2020-04-29 | a_malignant_neoplasm=1 | 286,284 | 3,372 | 8,039 |
| 2020-05-04 | a_malignant_neoplasm=0 | 5,210,049 | 7,192 | 114,111 |
| 2020-05-04 | a_malignant_neoplasm=1 | 285,766 | 3,546 | 8,196 |
By a_obesity.5y
survfit.t3.a_obesity.5y<-survfit(Surv(time, status) ~ a_obesity.5y,
data = r.healthy.death)
ggsurv.t3.a_obesity.5y<- list(
ggsurvplot(survfit.t3.a_obesity.5y, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t3.a_obesity.5y, fun = "cloglog", conf.int =TRUE))
ggsurv.t3.a_obesity.5y <-arrange_ggsurvplots(ggsurv.t3.a_obesity.5y, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t3.a_obesity.5y.png",ggsurv.t3.a_obesity.5y,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t3.a_obesity.5y.png")
at.risk.t3.a_obesity.5y<-ggsurvplot(survfit.t3.a_obesity.5y, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
mutate(date=as.Date("29/02/2020",
"%d/%m/%y")+time) %>%
select(date, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t3.a_obesity.5y,
col.names = c("Date", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Date | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 2020-02-29 | a_obesity.5y=0 | 4,689,927 | 0 | 0 |
| 2020-02-29 | a_obesity.5y=1 | 937,593 | 0 | 0 |
| 2020-03-05 | a_obesity.5y=0 | 4,688,791 | 440 | 963 |
| 2020-03-05 | a_obesity.5y=1 | 937,340 | 160 | 143 |
| 2020-03-10 | a_obesity.5y=0 | 4,687,286 | 785 | 2,303 |
| 2020-03-10 | a_obesity.5y=1 | 937,013 | 284 | 392 |
| 2020-03-15 | a_obesity.5y=0 | 4,683,041 | 1,355 | 6,061 |
| 2020-03-15 | a_obesity.5y=1 | 935,949 | 526 | 1,285 |
| 2020-03-20 | a_obesity.5y=0 | 4,669,182 | 1,863 | 22,479 |
| 2020-03-20 | a_obesity.5y=1 | 932,386 | 737 | 5,512 |
| 2020-03-25 | a_obesity.5y=0 | 4,653,385 | 2,255 | 38,081 |
| 2020-03-25 | a_obesity.5y=1 | 927,783 | 927 | 9,996 |
| 2020-03-30 | a_obesity.5y=0 | 4,641,111 | 2,629 | 50,452 |
| 2020-03-30 | a_obesity.5y=1 | 923,856 | 1,102 | 13,803 |
| 2020-04-04 | a_obesity.5y=0 | 4,626,405 | 3,510 | 61,080 |
| 2020-04-04 | a_obesity.5y=1 | 919,571 | 1,483 | 16,905 |
| 2020-04-09 | a_obesity.5y=0 | 4,617,461 | 4,195 | 70,549 |
| 2020-04-09 | a_obesity.5y=1 | 917,022 | 1,735 | 19,491 |
| 2020-04-14 | a_obesity.5y=0 | 4,611,157 | 4,463 | 76,713 |
| 2020-04-14 | a_obesity.5y=1 | 915,259 | 1,824 | 21,215 |
| 2020-04-19 | a_obesity.5y=0 | 4,601,856 | 5,530 | 83,094 |
| 2020-04-19 | a_obesity.5y=1 | 912,570 | 2,222 | 22,946 |
| 2020-04-24 | a_obesity.5y=0 | 4,594,963 | 6,500 | 89,759 |
| 2020-04-24 | a_obesity.5y=1 | 910,748 | 2,565 | 24,600 |
| 2020-04-29 | a_obesity.5y=0 | 4,590,483 | 7,308 | 93,614 |
| 2020-04-29 | a_obesity.5y=1 | 909,577 | 2,878 | 25,576 |
| 2020-05-04 | a_obesity.5y=0 | 4,587,126 | 7,722 | 96,153 |
| 2020-05-04 | a_obesity.5y=1 | 908,689 | 3,016 | 26,154 |
By a_t2_diabetes
survfit.t3.a_t2_diabetes<-survfit(Surv(time, status) ~ a_t2_diabetes,
data = r.healthy.death)
ggsurv.t3.a_t2_diabetes<- list(
ggsurvplot(survfit.t3.a_t2_diabetes, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t3.a_t2_diabetes, fun = "cloglog", conf.int =TRUE))
ggsurv.t3.a_t2_diabetes <-arrange_ggsurvplots(ggsurv.t3.a_t2_diabetes, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t3.a_t2_diabetes.png",ggsurv.t3.a_t2_diabetes,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t3.a_t2_diabetes.png")
at.risk.t3.a_t2_diabetes<-ggsurvplot(survfit.t3.a_t2_diabetes, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
mutate(date=as.Date("29/02/2020",
"%d/%m/%y")+time) %>%
select(date, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t3.a_t2_diabetes,
col.names = c("Date", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Date | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 2020-02-29 | a_t2_diabetes=0 | 5,302,576 | 0 | 0 |
| 2020-02-29 | a_t2_diabetes=1 | 324,944 | 0 | 0 |
| 2020-03-05 | a_t2_diabetes=0 | 5,301,335 | 468 | 1,059 |
| 2020-03-05 | a_t2_diabetes=1 | 324,796 | 132 | 47 |
| 2020-03-10 | a_t2_diabetes=0 | 5,299,700 | 832 | 2,547 |
| 2020-03-10 | a_t2_diabetes=1 | 324,599 | 237 | 148 |
| 2020-03-15 | a_t2_diabetes=0 | 5,294,891 | 1,437 | 6,874 |
| 2020-03-15 | a_t2_diabetes=1 | 324,099 | 444 | 472 |
| 2020-03-20 | a_t2_diabetes=0 | 5,278,543 | 1,980 | 26,365 |
| 2020-03-20 | a_t2_diabetes=1 | 323,025 | 620 | 1,626 |
| 2020-03-25 | a_t2_diabetes=0 | 5,259,662 | 2,428 | 44,961 |
| 2020-03-25 | a_t2_diabetes=1 | 321,506 | 754 | 3,116 |
| 2020-03-30 | a_t2_diabetes=0 | 5,244,936 | 2,832 | 59,802 |
| 2020-03-30 | a_t2_diabetes=1 | 320,031 | 899 | 4,453 |
| 2020-04-04 | a_t2_diabetes=0 | 5,227,607 | 3,799 | 72,418 |
| 2020-04-04 | a_t2_diabetes=1 | 318,369 | 1,194 | 5,567 |
| 2020-04-09 | a_t2_diabetes=0 | 5,217,221 | 4,507 | 83,478 |
| 2020-04-09 | a_t2_diabetes=1 | 317,262 | 1,423 | 6,562 |
| 2020-04-14 | a_t2_diabetes=0 | 5,209,983 | 4,779 | 90,642 |
| 2020-04-14 | a_t2_diabetes=1 | 316,433 | 1,508 | 7,286 |
| 2020-04-19 | a_t2_diabetes=0 | 5,199,365 | 5,902 | 97,934 |
| 2020-04-19 | a_t2_diabetes=1 | 315,061 | 1,850 | 8,106 |
| 2020-04-24 | a_t2_diabetes=0 | 5,191,510 | 6,935 | 105,583 |
| 2020-04-24 | a_t2_diabetes=1 | 314,201 | 2,130 | 8,776 |
| 2020-04-29 | a_t2_diabetes=0 | 5,186,384 | 7,838 | 110,055 |
| 2020-04-29 | a_t2_diabetes=1 | 313,676 | 2,348 | 9,135 |
| 2020-05-04 | a_t2_diabetes=0 | 5,182,569 | 8,268 | 112,978 |
| 2020-05-04 | a_t2_diabetes=1 | 313,246 | 2,470 | 9,329 |
Transition 4: From diagnosed with COVID-19 to hospitalised with COVID-19
overall
survfit.t4<-survfit(Surv(time, status) ~ 1,
data = r.diagnosis.hospitalised)
ggsurvplot.event<-ggsurvplot(survfit.t4, fun = "event", palette="black")
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.position = "none")
ggsave( "ggsurv.t4.png",print(ggsurvplot.event),
dpi=300,
width = 6, height = 5)
include_graphics("ggsurv.t4.png")
survfit.t4<-survfit(Surv(time, status) ~ 1,
data = r.diagnosis.hospitalised)
ggsurvplot.event<-ggsurvplot(survfit.t4, fun = "event", conf.int =TRUE)
at.risk.t4<-ggsurvplot(survfit.t4, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
select(time, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t4,
col.names = c("Time since diagnosis", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Time since diagnosis | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|
| 0 | 108,558 | 0 | 0 |
| 5 | 101,693 | 5,221 | 2,979 |
| 10 | 92,629 | 8,399 | 8,212 |
| 15 | 86,781 | 9,068 | 14,291 |
| 20 | 79,488 | 9,303 | 21,680 |
| 25 | 70,916 | 9,380 | 29,073 |
| 30 | 62,670 | 9,412 | 39,493 |
| 35 | 53,564 | 9,429 | 48,257 |
| 40 | 42,038 | 9,434 | 60,241 |
| 45 | 21,700 | 9,436 | 78,157 |
| 50 | 9,820 | 9,437 | 92,021 |
| 55 | 1,589 | 9,437 | 98,096 |
| 60 | 219 | 9,437 | 98,920 |
| 65 | 43 | 9,437 | 99,114 |
By gender
survfit.t4.gender<-survfit(Surv(time, status) ~ gender,
data = r.diagnosis.hospitalised)
ggsurvplot.event<-ggsurvplot(survfit.t4.gender, fun = "event", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.title = element_blank())
ggsurvplot.cloglog<-ggsurvplot(survfit.t4.gender, fun = "cloglog", conf.int =TRUE)
ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.position = "none")
ggsurv.t4.gender<- list(
ggsurvplot.event,
ggsurvplot.cloglog)
ggsurv.t4.gender <-arrange_ggsurvplots(ggsurv.t4.gender, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t4.gender.png",ggsurv.t4.gender,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t4.gender.png")
at.risk.t4.gender<-ggsurvplot(survfit.t4.gender, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
select(time, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t4.gender,
col.names = c("Time since diagnosis", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Time since diagnosis | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 0 | gender=Male | 44,565 | 0 | 0 |
| 0 | gender=Female | 63,993 | 0 | 0 |
| 5 | gender=Male | 41,060 | 2,888 | 1,257 |
| 5 | gender=Female | 60,633 | 2,333 | 1,722 |
| 10 | gender=Male | 36,965 | 4,593 | 3,311 |
| 10 | gender=Female | 55,664 | 3,806 | 4,901 |
| 15 | gender=Male | 34,632 | 4,924 | 5,609 |
| 15 | gender=Female | 52,149 | 4,144 | 8,682 |
| 20 | gender=Male | 31,995 | 5,019 | 8,264 |
| 20 | gender=Female | 47,493 | 4,284 | 13,416 |
| 25 | gender=Male | 28,740 | 5,049 | 11,065 |
| 25 | gender=Female | 42,176 | 4,331 | 18,008 |
| 30 | gender=Male | 25,654 | 5,068 | 15,009 |
| 30 | gender=Female | 37,016 | 4,344 | 24,484 |
| 35 | gender=Male | 22,249 | 5,077 | 18,286 |
| 35 | gender=Female | 31,315 | 4,352 | 29,971 |
| 40 | gender=Male | 17,693 | 5,078 | 23,134 |
| 40 | gender=Female | 24,345 | 4,356 | 37,107 |
| 45 | gender=Male | 9,102 | 5,079 | 30,680 |
| 45 | gender=Female | 12,598 | 4,357 | 47,477 |
| 50 | gender=Male | 4,068 | 5,080 | 36,603 |
| 50 | gender=Female | 5,752 | 4,357 | 55,418 |
| 55 | gender=Male | 594 | 5,080 | 39,107 |
| 55 | gender=Female | 995 | 4,357 | 58,989 |
| 60 | gender=Male | 65 | 5,080 | 39,427 |
| 60 | gender=Female | 154 | 4,357 | 59,493 |
| 65 | gender=Male | 7 | 5,080 | 39,484 |
| 65 | gender=Female | 36 | 4,357 | 59,630 |
By age
survfit.t4.age_gr<-survfit(Surv(time, status) ~ age_gr,
data = r.diagnosis.hospitalised %>% filter(age_gr!="Under 18")) # count less than 5, so omit
ggsurv.t4.age_gr<- list(
ggsurvplot(survfit.t4.age_gr, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t4.age_gr, fun = "cloglog", conf.int =TRUE))
ggsurv.t4.age_gr <-arrange_ggsurvplots(ggsurv.t4.age_gr, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t4.age_gr.png",ggsurv.t4.age_gr,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t4.age_gr.png")
at.risk.t4.age_gr<-ggsurvplot(survfit.t4.age_gr, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
select(time, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t4.age_gr,
col.names = c("Time since diagnosis", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Time since diagnosis | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 0 | age_gr=18 to 39 | 30,379 | 0 | 0 |
| 0 | age_gr=40 to 59 | 44,510 | 0 | 0 |
| 0 | age_gr=60 to 69 | 10,593 | 0 | 0 |
| 0 | age_gr=70 to 79 | 7,104 | 0 | 0 |
| 0 | age_gr=80 or older | 11,501 | 0 | 0 |
| 5 | age_gr=18 to 39 | 29,363 | 436 | 740 |
| 5 | age_gr=40 to 59 | 42,300 | 1,736 | 960 |
| 5 | age_gr=60 to 69 | 9,558 | 999 | 237 |
| 5 | age_gr=70 to 79 | 5,999 | 1,088 | 224 |
| 5 | age_gr=80 or older | 10,237 | 941 | 589 |
| 10 | age_gr=18 to 39 | 27,661 | 771 | 2,036 |
| 10 | age_gr=40 to 59 | 39,005 | 3,045 | 2,660 |
| 10 | age_gr=60 to 69 | 8,463 | 1,563 | 645 |
| 10 | age_gr=70 to 79 | 5,037 | 1,563 | 575 |
| 10 | age_gr=80 or older | 8,662 | 1,421 | 1,652 |
| 15 | age_gr=18 to 39 | 26,382 | 827 | 3,571 |
| 15 | age_gr=40 to 59 | 37,150 | 3,341 | 4,539 |
| 15 | age_gr=60 to 69 | 7,928 | 1,660 | 1,129 |
| 15 | age_gr=70 to 79 | 4,532 | 1,655 | 1,023 |
| 15 | age_gr=80 or older | 7,255 | 1,547 | 3,056 |
| 20 | age_gr=18 to 39 | 24,620 | 850 | 5,370 |
| 20 | age_gr=40 to 59 | 34,935 | 3,429 | 6,766 |
| 20 | age_gr=60 to 69 | 7,360 | 1,690 | 1,699 |
| 20 | age_gr=70 to 79 | 3,968 | 1,683 | 1,604 |
| 20 | age_gr=80 or older | 5,439 | 1,613 | 4,864 |
| 25 | age_gr=18 to 39 | 22,379 | 852 | 7,371 |
| 25 | age_gr=40 to 59 | 31,882 | 3,460 | 9,434 |
| 25 | age_gr=60 to 69 | 6,653 | 1,698 | 2,296 |
| 25 | age_gr=70 to 79 | 3,375 | 1,694 | 2,087 |
| 25 | age_gr=80 or older | 3,912 | 1,636 | 6,126 |
| 30 | age_gr=18 to 39 | 20,052 | 855 | 10,355 |
| 30 | age_gr=40 to 59 | 28,834 | 3,469 | 13,480 |
| 30 | age_gr=60 to 69 | 5,976 | 1,705 | 3,202 |
| 30 | age_gr=70 to 79 | 2,867 | 1,698 | 2,678 |
| 30 | age_gr=80 or older | 2,746 | 1,645 | 7,392 |
| 35 | age_gr=18 to 39 | 17,309 | 855 | 13,056 |
| 35 | age_gr=40 to 59 | 25,129 | 3,475 | 16,997 |
| 35 | age_gr=60 to 69 | 5,157 | 1,707 | 3,996 |
| 35 | age_gr=70 to 79 | 2,343 | 1,702 | 3,188 |
| 35 | age_gr=80 or older | 1,878 | 1,650 | 8,177 |
| 40 | age_gr=18 to 39 | 13,773 | 856 | 16,688 |
| 40 | age_gr=40 to 59 | 20,103 | 3,477 | 22,316 |
| 40 | age_gr=60 to 69 | 4,042 | 1,707 | 5,190 |
| 40 | age_gr=70 to 79 | 1,750 | 1,702 | 3,833 |
| 40 | age_gr=80 or older | 1,105 | 1,652 | 8,892 |
| 45 | age_gr=18 to 39 | 7,315 | 856 | 22,439 |
| 45 | age_gr=40 to 59 | 10,717 | 3,477 | 30,663 |
| 45 | age_gr=60 to 69 | 1,982 | 1,708 | 6,973 |
| 45 | age_gr=70 to 79 | 751 | 1,702 | 4,687 |
| 45 | age_gr=80 or older | 376 | 1,653 | 9,504 |
| 50 | age_gr=18 to 39 | 3,343 | 857 | 27,135 |
| 50 | age_gr=40 to 59 | 5,158 | 3,477 | 37,254 |
| 50 | age_gr=60 to 69 | 878 | 1,708 | 8,226 |
| 50 | age_gr=70 to 79 | 227 | 1,702 | 5,236 |
| 50 | age_gr=80 or older | 81 | 1,653 | 9,798 |
| 55 | age_gr=18 to 39 | 526 | 857 | 29,201 |
| 55 | age_gr=40 to 59 | 817 | 3,477 | 40,509 |
| 55 | age_gr=60 to 69 | 158 | 1,708 | 8,766 |
| 55 | age_gr=70 to 79 | 51 | 1,702 | 5,367 |
| 55 | age_gr=80 or older | 18 | 1,653 | 9,836 |
| 60 | age_gr=18 to 39 | 61 | 857 | 29,462 |
| 60 | age_gr=40 to 59 | 111 | 3,477 | 40,935 |
| 60 | age_gr=60 to 69 | 32 | 1,708 | 8,856 |
| 60 | age_gr=70 to 79 | 10 | 1,702 | 5,393 |
| 60 | age_gr=80 or older | 2 | 1,653 | 9,846 |
| 65 | age_gr=18 to 39 | 16 | 857 | 29,519 |
| 65 | age_gr=40 to 59 | 23 | 3,477 | 41,029 |
| 65 | age_gr=60 to 69 | 1 | 1,708 | 8,885 |
| 65 | age_gr=70 to 79 | 2 | 1,702 | 5,402 |
| 65 | age_gr=80 or older | 0 | 1,653 | 9,848 |
By charlson
survfit.t4.charlson<-survfit(Surv(time, status) ~ charlson,
data = r.diagnosis.hospitalised)
ggsurv.t4.charlson<- list(
ggsurvplot(survfit.t4.charlson, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t4.charlson, fun = "cloglog", conf.int =TRUE))
ggsurv.t4.charlson <-arrange_ggsurvplots(ggsurv.t4.charlson, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t4.charlson.png",ggsurv.t4.charlson,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t4.charlson.png")
at.risk.t4.charlson<-ggsurvplot(survfit.t4.charlson, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
select(time, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t4.charlson,
col.names = c("Time since diagnosis", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Time since diagnosis | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 0 | charlson=0 | 81,206 | 0 | 0 |
| 0 | charlson=1 | 10,635 | 0 | 0 |
| 0 | charlson=2 | 8,161 | 0 | 0 |
| 0 | charlson=3+ | 8,556 | 0 | 0 |
| 5 | charlson=0 | 77,101 | 2,945 | 1,970 |
| 5 | charlson=1 | 9,781 | 640 | 372 |
| 5 | charlson=2 | 7,316 | 748 | 253 |
| 5 | charlson=3+ | 7,495 | 888 | 384 |
| 10 | charlson=0 | 71,161 | 4,934 | 5,463 |
| 10 | charlson=1 | 8,736 | 1,016 | 978 |
| 10 | charlson=2 | 6,408 | 1,141 | 705 |
| 10 | charlson=3+ | 6,324 | 1,308 | 1,066 |
| 15 | charlson=0 | 67,499 | 5,319 | 9,424 |
| 15 | charlson=1 | 8,034 | 1,111 | 1,673 |
| 15 | charlson=2 | 5,841 | 1,215 | 1,247 |
| 15 | charlson=3+ | 5,407 | 1,423 | 1,947 |
| 20 | charlson=0 | 62,883 | 5,442 | 14,108 |
| 20 | charlson=1 | 7,135 | 1,148 | 2,578 |
| 20 | charlson=2 | 5,141 | 1,240 | 1,973 |
| 20 | charlson=3+ | 4,329 | 1,473 | 3,021 |
| 25 | charlson=0 | 56,963 | 5,485 | 19,313 |
| 25 | charlson=1 | 6,186 | 1,156 | 3,372 |
| 25 | charlson=2 | 4,405 | 1,254 | 2,573 |
| 25 | charlson=3+ | 3,362 | 1,485 | 3,815 |
| 30 | charlson=0 | 50,935 | 5,504 | 27,099 |
| 30 | charlson=1 | 5,322 | 1,157 | 4,420 |
| 30 | charlson=2 | 3,770 | 1,259 | 3,328 |
| 30 | charlson=3+ | 2,643 | 1,492 | 4,646 |
| 35 | charlson=0 | 43,951 | 5,508 | 33,863 |
| 35 | charlson=1 | 4,502 | 1,157 | 5,207 |
| 35 | charlson=2 | 3,149 | 1,265 | 3,929 |
| 35 | charlson=3+ | 1,962 | 1,499 | 5,258 |
| 40 | charlson=0 | 34,822 | 5,511 | 43,389 |
| 40 | charlson=1 | 3,474 | 1,157 | 6,298 |
| 40 | charlson=2 | 2,413 | 1,266 | 4,674 |
| 40 | charlson=3+ | 1,329 | 1,500 | 5,880 |
| 45 | charlson=0 | 18,158 | 5,512 | 58,135 |
| 45 | charlson=1 | 1,757 | 1,157 | 7,775 |
| 45 | charlson=2 | 1,216 | 1,266 | 5,731 |
| 45 | charlson=3+ | 569 | 1,501 | 6,516 |
| 50 | charlson=0 | 8,297 | 5,513 | 69,732 |
| 50 | charlson=1 | 797 | 1,157 | 8,877 |
| 50 | charlson=2 | 508 | 1,266 | 6,519 |
| 50 | charlson=3+ | 218 | 1,501 | 6,893 |
| 55 | charlson=0 | 1,325 | 5,513 | 74,848 |
| 55 | charlson=1 | 128 | 1,157 | 9,388 |
| 55 | charlson=2 | 101 | 1,266 | 6,831 |
| 55 | charlson=3+ | 35 | 1,501 | 7,029 |
| 60 | charlson=0 | 186 | 5,513 | 75,522 |
| 60 | charlson=1 | 14 | 1,157 | 9,464 |
| 60 | charlson=2 | 16 | 1,266 | 6,882 |
| 60 | charlson=3+ | 3 | 1,501 | 7,052 |
| 65 | charlson=0 | 37 | 5,513 | 75,686 |
| 65 | charlson=1 | 3 | 1,157 | 9,478 |
| 65 | charlson=2 | 3 | 1,266 | 6,895 |
| 65 | charlson=3+ | 0 | 1,501 | 7,055 |
By a_autoimmune_condition
survfit.t4.a_autoimmune_condition<-survfit(Surv(time, status) ~ a_autoimmune_condition,
data = r.diagnosis.hospitalised)
ggsurv.t4.a_autoimmune_condition<- list(
ggsurvplot(survfit.t4.a_autoimmune_condition, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t4.a_autoimmune_condition, fun = "cloglog", conf.int =TRUE))
ggsurv.t4.a_autoimmune_condition <-arrange_ggsurvplots(ggsurv.t4.a_autoimmune_condition, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t4.a_autoimmune_condition.png",ggsurv.t4.a_autoimmune_condition,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t4.a_autoimmune_condition.png")
at.risk.t4.a_autoimmune_condition<-ggsurvplot(survfit.t4.a_autoimmune_condition, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
select(time, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t4.a_autoimmune_condition,
col.names = c("Time since diagnosis", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Time since diagnosis | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 0 | a_autoimmune_condition=0 | 101,670 | 0 | 0 |
| 0 | a_autoimmune_condition=1 | 6,888 | 0 | 0 |
| 5 | a_autoimmune_condition=0 | 95,345 | 4,769 | 2,769 |
| 5 | a_autoimmune_condition=1 | 6,348 | 452 | 210 |
| 10 | a_autoimmune_condition=0 | 86,932 | 7,700 | 7,672 |
| 10 | a_autoimmune_condition=1 | 5,697 | 699 | 540 |
| 15 | a_autoimmune_condition=0 | 81,472 | 8,310 | 13,352 |
| 15 | a_autoimmune_condition=1 | 5,309 | 758 | 939 |
| 20 | a_autoimmune_condition=0 | 74,696 | 8,526 | 20,205 |
| 20 | a_autoimmune_condition=1 | 4,792 | 777 | 1,475 |
| 25 | a_autoimmune_condition=0 | 66,728 | 8,595 | 27,108 |
| 25 | a_autoimmune_condition=1 | 4,188 | 785 | 1,965 |
| 30 | a_autoimmune_condition=0 | 58,975 | 8,626 | 36,917 |
| 30 | a_autoimmune_condition=1 | 3,695 | 786 | 2,576 |
| 35 | a_autoimmune_condition=0 | 50,434 | 8,643 | 45,141 |
| 35 | a_autoimmune_condition=1 | 3,130 | 786 | 3,116 |
| 40 | a_autoimmune_condition=0 | 39,577 | 8,648 | 56,430 |
| 40 | a_autoimmune_condition=1 | 2,461 | 786 | 3,811 |
| 45 | a_autoimmune_condition=0 | 20,389 | 8,650 | 73,315 |
| 45 | a_autoimmune_condition=1 | 1,311 | 786 | 4,842 |
| 50 | a_autoimmune_condition=0 | 9,233 | 8,651 | 86,344 |
| 50 | a_autoimmune_condition=1 | 587 | 786 | 5,677 |
| 55 | a_autoimmune_condition=0 | 1,493 | 8,651 | 92,053 |
| 55 | a_autoimmune_condition=1 | 96 | 786 | 6,043 |
| 60 | a_autoimmune_condition=0 | 204 | 8,651 | 92,833 |
| 60 | a_autoimmune_condition=1 | 15 | 786 | 6,087 |
| 65 | a_autoimmune_condition=0 | 43 | 8,651 | 93,012 |
| 65 | a_autoimmune_condition=1 | 0 | 786 | 6,102 |
By a_chronic_kidney_disease
survfit.t4.a_chronic_kidney_disease<-survfit(Surv(time, status) ~ a_chronic_kidney_disease,
data = r.diagnosis.hospitalised)
ggsurv.t4.a_chronic_kidney_disease<- list(
ggsurvplot(survfit.t4.a_chronic_kidney_disease, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t4.a_chronic_kidney_disease, fun = "cloglog", conf.int =TRUE))
ggsurv.t4.a_chronic_kidney_disease <-arrange_ggsurvplots(ggsurv.t4.a_chronic_kidney_disease, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t4.a_chronic_kidney_disease.png",ggsurv.t4.a_chronic_kidney_disease,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t4.a_chronic_kidney_disease.png")
at.risk.t4.a_chronic_kidney_disease<-ggsurvplot(survfit.t4.a_chronic_kidney_disease, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
select(time, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t4.a_chronic_kidney_disease,
col.names = c("Time since diagnosis", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Time since diagnosis | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 0 | a_chronic_kidney_disease=0 | 102,750 | 0 | 0 |
| 0 | a_chronic_kidney_disease=1 | 5,808 | 0 | 0 |
| 5 | a_chronic_kidney_disease=0 | 96,626 | 4,590 | 2,732 |
| 5 | a_chronic_kidney_disease=1 | 5,067 | 631 | 247 |
| 10 | a_chronic_kidney_disease=0 | 88,371 | 7,466 | 7,488 |
| 10 | a_chronic_kidney_disease=1 | 4,258 | 933 | 724 |
| 15 | a_chronic_kidney_disease=0 | 83,152 | 8,062 | 12,960 |
| 15 | a_chronic_kidney_disease=1 | 3,629 | 1,006 | 1,331 |
| 20 | a_chronic_kidney_disease=0 | 76,579 | 8,269 | 19,636 |
| 20 | a_chronic_kidney_disease=1 | 2,909 | 1,034 | 2,044 |
| 25 | a_chronic_kidney_disease=0 | 68,688 | 8,335 | 26,477 |
| 25 | a_chronic_kidney_disease=1 | 2,228 | 1,045 | 2,596 |
| 30 | a_chronic_kidney_disease=0 | 60,934 | 8,361 | 36,337 |
| 30 | a_chronic_kidney_disease=1 | 1,736 | 1,051 | 3,156 |
| 35 | a_chronic_kidney_disease=0 | 52,277 | 8,372 | 44,686 |
| 35 | a_chronic_kidney_disease=1 | 1,287 | 1,057 | 3,571 |
| 40 | a_chronic_kidney_disease=0 | 41,173 | 8,377 | 56,251 |
| 40 | a_chronic_kidney_disease=1 | 865 | 1,057 | 3,990 |
| 45 | a_chronic_kidney_disease=0 | 21,343 | 8,378 | 73,744 |
| 45 | a_chronic_kidney_disease=1 | 357 | 1,058 | 4,413 |
| 50 | a_chronic_kidney_disease=0 | 9,714 | 8,379 | 87,346 |
| 50 | a_chronic_kidney_disease=1 | 106 | 1,058 | 4,675 |
| 55 | a_chronic_kidney_disease=0 | 1,576 | 8,379 | 93,354 |
| 55 | a_chronic_kidney_disease=1 | 13 | 1,058 | 4,742 |
| 60 | a_chronic_kidney_disease=0 | 218 | 8,379 | 94,171 |
| 60 | a_chronic_kidney_disease=1 | 1 | 1,058 | 4,749 |
| 65 | a_chronic_kidney_disease=0 | 43 | 8,379 | 94,364 |
| 65 | a_chronic_kidney_disease=1 | 0 | 1,058 | 4,750 |
By a_copd
survfit.t4.a_copd<-survfit(Surv(time, status) ~ a_copd,
data = r.diagnosis.hospitalised)
ggsurv.t4.a_copd<- list(
ggsurvplot(survfit.t4.a_copd, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t4.a_copd, fun = "cloglog", conf.int =TRUE))
ggsurv.t4.a_copd <-arrange_ggsurvplots(ggsurv.t4.a_copd, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t4.a_copd.png",ggsurv.t4.a_copd,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t4.a_copd.png")
at.risk.t4.a_copd<-ggsurvplot(survfit.t4.a_copd, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
select(time, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t4.a_copd,
col.names = c("Time since diagnosis", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Time since diagnosis | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 0 | a_copd=0 | 105,705 | 0 | 0 |
| 0 | a_copd=1 | 2,853 | 0 | 0 |
| 5 | a_copd=0 | 99,229 | 4,897 | 2,855 |
| 5 | a_copd=1 | 2,464 | 324 | 124 |
| 10 | a_copd=0 | 90,517 | 7,934 | 7,901 |
| 10 | a_copd=1 | 2,112 | 465 | 311 |
| 15 | a_copd=0 | 84,909 | 8,572 | 13,750 |
| 15 | a_copd=1 | 1,872 | 496 | 541 |
| 20 | a_copd=0 | 77,887 | 8,794 | 20,843 |
| 20 | a_copd=1 | 1,601 | 509 | 837 |
| 25 | a_copd=0 | 69,597 | 8,867 | 28,025 |
| 25 | a_copd=1 | 1,319 | 513 | 1,048 |
| 30 | a_copd=0 | 61,559 | 8,897 | 38,195 |
| 30 | a_copd=1 | 1,111 | 515 | 1,298 |
| 35 | a_copd=0 | 52,678 | 8,914 | 46,746 |
| 35 | a_copd=1 | 886 | 515 | 1,511 |
| 40 | a_copd=0 | 41,383 | 8,919 | 58,487 |
| 40 | a_copd=1 | 655 | 515 | 1,754 |
| 45 | a_copd=0 | 21,377 | 8,921 | 76,134 |
| 45 | a_copd=1 | 323 | 515 | 2,023 |
| 50 | a_copd=0 | 9,683 | 8,922 | 89,790 |
| 50 | a_copd=1 | 137 | 515 | 2,231 |
| 55 | a_copd=0 | 1,566 | 8,922 | 95,771 |
| 55 | a_copd=1 | 23 | 515 | 2,325 |
| 60 | a_copd=0 | 216 | 8,922 | 96,585 |
| 60 | a_copd=1 | 3 | 515 | 2,335 |
| 65 | a_copd=0 | 42 | 8,922 | 96,776 |
| 65 | a_copd=1 | 1 | 515 | 2,338 |
By a_dementia
survfit.t4.a_dementia<-survfit(Surv(time, status) ~ a_dementia,
data = r.diagnosis.hospitalised)
ggsurv.t4.a_dementia<- list(
ggsurvplot(survfit.t4.a_dementia, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t4.a_dementia, fun = "cloglog", conf.int =TRUE))
ggsurv.t4.a_dementia <-arrange_ggsurvplots(ggsurv.t4.a_dementia, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t4.a_dementia.png",ggsurv.t4.a_dementia,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t4.a_dementia.png")
at.risk.t4.a_dementia<-ggsurvplot(survfit.t4.a_dementia, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
select(time, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t4.a_dementia,
col.names = c("Time since diagnosis", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Time since diagnosis | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 0 | a_dementia=0 | 103,783 | 0 | 0 |
| 0 | a_dementia=1 | 4,775 | 0 | 0 |
| 5 | a_dementia=0 | 97,359 | 4,961 | 2,704 |
| 5 | a_dementia=1 | 4,334 | 260 | 275 |
| 10 | a_dementia=0 | 88,891 | 8,010 | 7,471 |
| 10 | a_dementia=1 | 3,738 | 389 | 741 |
| 15 | a_dementia=0 | 83,667 | 8,621 | 12,921 |
| 15 | a_dementia=1 | 3,114 | 447 | 1,370 |
| 20 | a_dementia=0 | 77,279 | 8,830 | 19,398 |
| 20 | a_dementia=1 | 2,209 | 473 | 2,282 |
| 25 | a_dementia=0 | 69,456 | 8,898 | 26,152 |
| 25 | a_dementia=1 | 1,460 | 482 | 2,921 |
| 30 | a_dementia=0 | 61,733 | 8,928 | 36,024 |
| 30 | a_dementia=1 | 937 | 484 | 3,469 |
| 35 | a_dementia=0 | 52,992 | 8,944 | 44,472 |
| 35 | a_dementia=1 | 572 | 485 | 3,785 |
| 40 | a_dementia=0 | 41,754 | 8,948 | 56,193 |
| 40 | a_dementia=1 | 284 | 486 | 4,048 |
| 45 | a_dementia=0 | 21,617 | 8,950 | 73,941 |
| 45 | a_dementia=1 | 83 | 486 | 4,216 |
| 50 | a_dementia=0 | 9,806 | 8,951 | 87,741 |
| 50 | a_dementia=1 | 14 | 486 | 4,280 |
| 55 | a_dementia=0 | 1,585 | 8,951 | 93,811 |
| 55 | a_dementia=1 | 4 | 486 | 4,285 |
| 60 | a_dementia=0 | 219 | 8,951 | 94,631 |
| 60 | a_dementia=1 | 0 | 486 | 4,289 |
| 65 | a_dementia=0 | 43 | 8,951 | 94,825 |
| 65 | a_dementia=1 | 0 | 486 | 4,289 |
By a_heart_disease
survfit.t4.a_heart_disease<-survfit(Surv(time, status) ~ a_heart_disease,
data = r.diagnosis.hospitalised)
ggsurv.t4.a_heart_disease<- list(
ggsurvplot(survfit.t4.a_heart_disease, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t4.a_heart_disease, fun = "cloglog", conf.int =TRUE))
ggsurv.t4.a_heart_disease <-arrange_ggsurvplots(ggsurv.t4.a_heart_disease, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t4.a_heart_disease.png",ggsurv.t4.a_heart_disease,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t4.a_heart_disease.png")
at.risk.t4.a_heart_disease<-ggsurvplot(survfit.t4.a_heart_disease, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
select(time, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t4.a_heart_disease,
col.names = c("Time since diagnosis", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Time since diagnosis | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 0 | a_heart_disease=0 | 95,108 | 0 | 0 |
| 0 | a_heart_disease=1 | 13,450 | 0 | 0 |
| 5 | a_heart_disease=0 | 89,677 | 3,984 | 2,511 |
| 5 | a_heart_disease=1 | 12,016 | 1,237 | 468 |
| 10 | a_heart_disease=0 | 82,233 | 6,518 | 6,881 |
| 10 | a_heart_disease=1 | 10,396 | 1,881 | 1,331 |
| 15 | a_heart_disease=0 | 77,523 | 7,030 | 11,865 |
| 15 | a_heart_disease=1 | 9,258 | 2,038 | 2,426 |
| 20 | a_heart_disease=0 | 71,544 | 7,199 | 17,933 |
| 20 | a_heart_disease=1 | 7,944 | 2,104 | 3,747 |
| 25 | a_heart_disease=0 | 64,261 | 7,265 | 24,274 |
| 25 | a_heart_disease=1 | 6,655 | 2,115 | 4,799 |
| 30 | a_heart_disease=0 | 57,085 | 7,288 | 33,436 |
| 30 | a_heart_disease=1 | 5,585 | 2,124 | 6,057 |
| 35 | a_heart_disease=0 | 48,999 | 7,300 | 41,230 |
| 35 | a_heart_disease=1 | 4,565 | 2,129 | 7,027 |
| 40 | a_heart_disease=0 | 38,635 | 7,303 | 51,999 |
| 40 | a_heart_disease=1 | 3,403 | 2,131 | 8,242 |
| 45 | a_heart_disease=0 | 20,082 | 7,304 | 68,398 |
| 45 | a_heart_disease=1 | 1,618 | 2,132 | 9,759 |
| 50 | a_heart_disease=0 | 9,139 | 7,305 | 81,206 |
| 50 | a_heart_disease=1 | 681 | 2,132 | 10,815 |
| 55 | a_heart_disease=0 | 1,462 | 7,305 | 86,863 |
| 55 | a_heart_disease=1 | 127 | 2,132 | 11,233 |
| 60 | a_heart_disease=0 | 210 | 7,305 | 87,610 |
| 60 | a_heart_disease=1 | 9 | 2,132 | 11,310 |
| 65 | a_heart_disease=0 | 42 | 7,305 | 87,796 |
| 65 | a_heart_disease=1 | 1 | 2,132 | 11,318 |
By a_hyperlipidemia
survfit.t4.a_hyperlipidemia<-survfit(Surv(time, status) ~ a_hyperlipidemia,
data = r.diagnosis.hospitalised)
ggsurv.t4.a_hyperlipidemia<- list(
ggsurvplot(survfit.t4.a_hyperlipidemia, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t4.a_hyperlipidemia, fun = "cloglog", conf.int =TRUE))
ggsurv.t4.a_hyperlipidemia <-arrange_ggsurvplots(ggsurv.t4.a_hyperlipidemia, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t4.a_hyperlipidemia.png",ggsurv.t4.a_hyperlipidemia,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t4.a_hyperlipidemia.png")
at.risk.t4.a_hyperlipidemia<-ggsurvplot(survfit.t4.a_hyperlipidemia, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
select(time, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t4.a_hyperlipidemia,
col.names = c("Time since diagnosis", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Time since diagnosis | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 0 | a_hyperlipidemia=0 | 96,745 | 0 | 0 |
| 0 | a_hyperlipidemia=1 | 11,813 | 0 | 0 |
| 5 | a_hyperlipidemia=0 | 90,928 | 4,292 | 2,647 |
| 5 | a_hyperlipidemia=1 | 10,765 | 929 | 332 |
| 10 | a_hyperlipidemia=0 | 83,074 | 6,973 | 7,299 |
| 10 | a_hyperlipidemia=1 | 9,555 | 1,426 | 913 |
| 15 | a_hyperlipidemia=0 | 77,891 | 7,532 | 12,722 |
| 15 | a_hyperlipidemia=1 | 8,890 | 1,536 | 1,569 |
| 20 | a_hyperlipidemia=0 | 71,438 | 7,726 | 19,270 |
| 20 | a_hyperlipidemia=1 | 8,050 | 1,577 | 2,410 |
| 25 | a_hyperlipidemia=0 | 63,804 | 7,793 | 25,888 |
| 25 | a_hyperlipidemia=1 | 7,112 | 1,587 | 3,185 |
| 30 | a_hyperlipidemia=0 | 56,427 | 7,816 | 35,219 |
| 30 | a_hyperlipidemia=1 | 6,243 | 1,596 | 4,274 |
| 35 | a_hyperlipidemia=0 | 48,275 | 7,831 | 43,041 |
| 35 | a_hyperlipidemia=1 | 5,289 | 1,598 | 5,216 |
| 40 | a_hyperlipidemia=0 | 37,960 | 7,836 | 53,769 |
| 40 | a_hyperlipidemia=1 | 4,078 | 1,598 | 6,472 |
| 45 | a_hyperlipidemia=0 | 19,645 | 7,837 | 69,927 |
| 45 | a_hyperlipidemia=1 | 2,055 | 1,599 | 8,230 |
| 50 | a_hyperlipidemia=0 | 8,909 | 7,838 | 82,463 |
| 50 | a_hyperlipidemia=1 | 911 | 1,599 | 9,558 |
| 55 | a_hyperlipidemia=0 | 1,451 | 7,838 | 87,967 |
| 55 | a_hyperlipidemia=1 | 138 | 1,599 | 10,129 |
| 60 | a_hyperlipidemia=0 | 205 | 7,838 | 88,718 |
| 60 | a_hyperlipidemia=1 | 14 | 1,599 | 10,202 |
| 65 | a_hyperlipidemia=0 | 42 | 7,838 | 88,900 |
| 65 | a_hyperlipidemia=1 | 1 | 1,599 | 10,214 |
By a_hypertension
survfit.t4.a_hypertension<-survfit(Surv(time, status) ~ a_hypertension,
data = r.diagnosis.hospitalised)
ggsurv.t4.a_hypertension<- list(
ggsurvplot(survfit.t4.a_hypertension, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t4.a_hypertension, fun = "cloglog", conf.int =TRUE))
ggsurv.t4.a_hypertension <-arrange_ggsurvplots(ggsurv.t4.a_hypertension, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t4.a_hypertension.png",ggsurv.t4.a_hypertension,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t4.a_hypertension.png")
at.risk.t4.a_hypertension<-ggsurvplot(survfit.t4.a_hypertension, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
select(time, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t4.a_hypertension,
col.names = c("Time since diagnosis", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Time since diagnosis | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 0 | a_hypertension=0 | 92,240 | 0 | 0 |
| 0 | a_hypertension=1 | 16,318 | 0 | 0 |
| 5 | a_hypertension=0 | 86,936 | 3,832 | 2,472 |
| 5 | a_hypertension=1 | 14,757 | 1,389 | 507 |
| 10 | a_hypertension=0 | 79,660 | 6,263 | 6,831 |
| 10 | a_hypertension=1 | 12,969 | 2,136 | 1,381 |
| 15 | a_hypertension=0 | 74,974 | 6,756 | 11,784 |
| 15 | a_hypertension=1 | 11,807 | 2,312 | 2,507 |
| 20 | a_hypertension=0 | 69,146 | 6,932 | 17,705 |
| 20 | a_hypertension=1 | 10,342 | 2,371 | 3,975 |
| 25 | a_hypertension=0 | 62,063 | 6,986 | 23,877 |
| 25 | a_hypertension=1 | 8,853 | 2,394 | 5,196 |
| 30 | a_hypertension=0 | 55,092 | 7,008 | 32,786 |
| 30 | a_hypertension=1 | 7,578 | 2,404 | 6,707 |
| 35 | a_hypertension=0 | 47,187 | 7,018 | 40,394 |
| 35 | a_hypertension=1 | 6,377 | 2,411 | 7,863 |
| 40 | a_hypertension=0 | 37,157 | 7,022 | 50,833 |
| 40 | a_hypertension=1 | 4,881 | 2,412 | 9,408 |
| 45 | a_hypertension=0 | 19,258 | 7,024 | 66,623 |
| 45 | a_hypertension=1 | 2,442 | 2,412 | 11,534 |
| 50 | a_hypertension=0 | 8,743 | 7,025 | 78,895 |
| 50 | a_hypertension=1 | 1,077 | 2,412 | 13,126 |
| 55 | a_hypertension=0 | 1,418 | 7,025 | 84,313 |
| 55 | a_hypertension=1 | 171 | 2,412 | 13,783 |
| 60 | a_hypertension=0 | 192 | 7,025 | 85,039 |
| 60 | a_hypertension=1 | 27 | 2,412 | 13,881 |
| 65 | a_hypertension=0 | 40 | 7,025 | 85,208 |
| 65 | a_hypertension=1 | 3 | 2,412 | 13,906 |
By a_malignant_neoplasm
survfit.t4.a_malignant_neoplasm<-survfit(Surv(time, status) ~ a_malignant_neoplasm,
data = r.diagnosis.hospitalised)
ggsurv.t4.a_malignant_neoplasm<- list(
ggsurvplot(survfit.t4.a_malignant_neoplasm, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t4.a_malignant_neoplasm, fun = "cloglog", conf.int =TRUE))
ggsurv.t4.a_malignant_neoplasm <-arrange_ggsurvplots(ggsurv.t4.a_malignant_neoplasm, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t4.a_malignant_neoplasm.png",ggsurv.t4.a_malignant_neoplasm,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t4.a_malignant_neoplasm.png")
at.risk.t4.a_malignant_neoplasm<-ggsurvplot(survfit.t4.a_malignant_neoplasm, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
select(time, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t4.a_malignant_neoplasm,
col.names = c("Time since diagnosis", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Time since diagnosis | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 0 | a_malignant_neoplasm=0 | 101,936 | 0 | 0 |
| 0 | a_malignant_neoplasm=1 | 6,622 | 0 | 0 |
| 5 | a_malignant_neoplasm=0 | 95,773 | 4,584 | 2,764 |
| 5 | a_malignant_neoplasm=1 | 5,920 | 637 | 215 |
| 10 | a_malignant_neoplasm=0 | 87,509 | 7,434 | 7,595 |
| 10 | a_malignant_neoplasm=1 | 5,120 | 965 | 617 |
| 15 | a_malignant_neoplasm=0 | 82,151 | 8,039 | 13,187 |
| 15 | a_malignant_neoplasm=1 | 4,630 | 1,029 | 1,104 |
| 20 | a_malignant_neoplasm=0 | 75,465 | 8,244 | 19,994 |
| 20 | a_malignant_neoplasm=1 | 4,023 | 1,059 | 1,686 |
| 25 | a_malignant_neoplasm=0 | 67,455 | 8,315 | 26,921 |
| 25 | a_malignant_neoplasm=1 | 3,461 | 1,065 | 2,152 |
| 30 | a_malignant_neoplasm=0 | 59,706 | 8,342 | 36,721 |
| 30 | a_malignant_neoplasm=1 | 2,964 | 1,070 | 2,772 |
| 35 | a_malignant_neoplasm=0 | 51,144 | 8,355 | 44,985 |
| 35 | a_malignant_neoplasm=1 | 2,420 | 1,074 | 3,272 |
| 40 | a_malignant_neoplasm=0 | 40,198 | 8,360 | 56,373 |
| 40 | a_malignant_neoplasm=1 | 1,840 | 1,074 | 3,868 |
| 45 | a_malignant_neoplasm=0 | 20,784 | 8,361 | 73,482 |
| 45 | a_malignant_neoplasm=1 | 916 | 1,075 | 4,675 |
| 50 | a_malignant_neoplasm=0 | 9,416 | 8,362 | 86,779 |
| 50 | a_malignant_neoplasm=1 | 404 | 1,075 | 5,242 |
| 55 | a_malignant_neoplasm=0 | 1,511 | 8,362 | 92,597 |
| 55 | a_malignant_neoplasm=1 | 78 | 1,075 | 5,499 |
| 60 | a_malignant_neoplasm=0 | 207 | 8,362 | 93,384 |
| 60 | a_malignant_neoplasm=1 | 12 | 1,075 | 5,536 |
| 65 | a_malignant_neoplasm=0 | 40 | 8,362 | 93,567 |
| 65 | a_malignant_neoplasm=1 | 3 | 1,075 | 5,547 |
By a_obesity.5y
survfit.t4.a_obesity.5y<-survfit(Surv(time, status) ~ a_obesity.5y,
data = r.diagnosis.hospitalised)
ggsurv.t4.a_obesity.5y<- list(
ggsurvplot(survfit.t4.a_obesity.5y, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t4.a_obesity.5y, fun = "cloglog", conf.int =TRUE))
ggsurv.t4.a_obesity.5y <-arrange_ggsurvplots(ggsurv.t4.a_obesity.5y, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t4.a_obesity.5y.png",ggsurv.t4.a_obesity.5y,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t4.a_obesity.5y.png")
at.risk.t4.a_obesity.5y<-ggsurvplot(survfit.t4.a_obesity.5y, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
select(time, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t4.a_obesity.5y,
col.names = c("Time since diagnosis", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Time since diagnosis | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 0 | a_obesity.5y=0 | 86,219 | 0 | 0 |
| 0 | a_obesity.5y=1 | 22,339 | 0 | 0 |
| 5 | a_obesity.5y=0 | 81,448 | 3,293 | 2,385 |
| 5 | a_obesity.5y=1 | 20,245 | 1,928 | 594 |
| 10 | a_obesity.5y=0 | 74,764 | 5,427 | 6,535 |
| 10 | a_obesity.5y=1 | 17,865 | 2,972 | 1,677 |
| 15 | a_obesity.5y=0 | 70,210 | 5,898 | 11,372 |
| 15 | a_obesity.5y=1 | 16,571 | 3,170 | 2,919 |
| 20 | a_obesity.5y=0 | 64,437 | 6,060 | 17,201 |
| 20 | a_obesity.5y=1 | 15,051 | 3,243 | 4,479 |
| 25 | a_obesity.5y=0 | 57,672 | 6,112 | 23,104 |
| 25 | a_obesity.5y=1 | 13,244 | 3,268 | 5,969 |
| 30 | a_obesity.5y=0 | 51,036 | 6,132 | 31,485 |
| 30 | a_obesity.5y=1 | 11,634 | 3,280 | 8,008 |
| 35 | a_obesity.5y=0 | 43,740 | 6,145 | 38,520 |
| 35 | a_obesity.5y=1 | 9,824 | 3,284 | 9,737 |
| 40 | a_obesity.5y=0 | 34,461 | 6,149 | 48,095 |
| 40 | a_obesity.5y=1 | 7,577 | 3,285 | 12,146 |
| 45 | a_obesity.5y=0 | 17,987 | 6,151 | 62,679 |
| 45 | a_obesity.5y=1 | 3,713 | 3,285 | 15,478 |
| 50 | a_obesity.5y=0 | 8,286 | 6,151 | 74,068 |
| 50 | a_obesity.5y=1 | 1,534 | 3,286 | 17,953 |
| 55 | a_obesity.5y=0 | 1,328 | 6,151 | 79,213 |
| 55 | a_obesity.5y=1 | 261 | 3,286 | 18,883 |
| 60 | a_obesity.5y=0 | 178 | 6,151 | 79,906 |
| 60 | a_obesity.5y=1 | 41 | 3,286 | 19,014 |
| 65 | a_obesity.5y=0 | 35 | 6,151 | 80,063 |
| 65 | a_obesity.5y=1 | 8 | 3,286 | 19,051 |
By a_t2_diabetes
survfit.t4.a_t2_diabetes<-survfit(Surv(time, status) ~ a_t2_diabetes,
data = r.diagnosis.hospitalised)
ggsurv.t4.a_t2_diabetes<- list(
ggsurvplot(survfit.t4.a_t2_diabetes, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t4.a_t2_diabetes, fun = "cloglog", conf.int =TRUE))
ggsurv.t4.a_t2_diabetes <-arrange_ggsurvplots(ggsurv.t4.a_t2_diabetes, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t4.a_t2_diabetes.png",ggsurv.t4.a_t2_diabetes,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t4.a_t2_diabetes.png")
at.risk.t4.a_t2_diabetes<-ggsurvplot(survfit.t4.a_t2_diabetes, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
select(time, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t4.a_t2_diabetes,
col.names = c("Time since diagnosis", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Time since diagnosis | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 0 | a_t2_diabetes=0 | 101,182 | 0 | 0 |
| 0 | a_t2_diabetes=1 | 7,376 | 0 | 0 |
| 5 | a_t2_diabetes=0 | 95,248 | 4,377 | 2,719 |
| 5 | a_t2_diabetes=1 | 6,445 | 844 | 260 |
| 10 | a_t2_diabetes=0 | 87,154 | 7,126 | 7,504 |
| 10 | a_t2_diabetes=1 | 5,475 | 1,273 | 708 |
| 15 | a_t2_diabetes=0 | 81,891 | 7,701 | 13,019 |
| 15 | a_t2_diabetes=1 | 4,890 | 1,367 | 1,272 |
| 20 | a_t2_diabetes=0 | 75,297 | 7,902 | 19,715 |
| 20 | a_t2_diabetes=1 | 4,191 | 1,401 | 1,965 |
| 25 | a_t2_diabetes=0 | 67,413 | 7,971 | 26,535 |
| 25 | a_t2_diabetes=1 | 3,503 | 1,409 | 2,538 |
| 30 | a_t2_diabetes=0 | 59,718 | 7,996 | 36,312 |
| 30 | a_t2_diabetes=1 | 2,952 | 1,416 | 3,181 |
| 35 | a_t2_diabetes=0 | 51,144 | 8,009 | 44,560 |
| 35 | a_t2_diabetes=1 | 2,420 | 1,420 | 3,697 |
| 40 | a_t2_diabetes=0 | 40,250 | 8,012 | 55,916 |
| 40 | a_t2_diabetes=1 | 1,788 | 1,422 | 4,325 |
| 45 | a_t2_diabetes=0 | 20,890 | 8,014 | 72,975 |
| 45 | a_t2_diabetes=1 | 810 | 1,422 | 5,182 |
| 50 | a_t2_diabetes=0 | 9,501 | 8,015 | 86,305 |
| 50 | a_t2_diabetes=1 | 319 | 1,422 | 5,716 |
| 55 | a_t2_diabetes=0 | 1,531 | 8,015 | 92,182 |
| 55 | a_t2_diabetes=1 | 58 | 1,422 | 5,914 |
| 60 | a_t2_diabetes=0 | 215 | 8,015 | 92,970 |
| 60 | a_t2_diabetes=1 | 4 | 1,422 | 5,950 |
| 65 | a_t2_diabetes=0 | 43 | 8,015 | 93,160 |
| 65 | a_t2_diabetes=1 | 0 | 1,422 | 5,954 |
Transition 5: From diagnosed with COVID-19 to death
overall
survfit.t5<-survfit(Surv(time, status) ~ 1,
data = r.diagnosis.death)
ggsurvplot.event<-ggsurvplot(survfit.t5, fun = "event", palette="black")
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.position = "none")
ggsave( "ggsurv.t5.png",print(ggsurvplot.event),
dpi=300,
width = 6, height = 5)
include_graphics("ggsurv.t5.png")
survfit.t5<-survfit(Surv(time, status) ~ 1,
data = r.diagnosis.death)
ggsurvplot.event<-ggsurvplot(survfit.t5, fun = "event", conf.int =TRUE)
at.risk.t5<-ggsurvplot(survfit.t5, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
select(time, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t5,
col.names = c("Time since diagnosis", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Time since diagnosis | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|
| 0 | 108,558 | 0 | 0 |
| 5 | 101,693 | 354 | 7,846 |
| 10 | 92,629 | 941 | 15,670 |
| 15 | 86,781 | 1,544 | 21,815 |
| 20 | 79,488 | 2,025 | 28,958 |
| 25 | 70,916 | 2,344 | 36,109 |
| 30 | 62,670 | 2,526 | 46,379 |
| 35 | 53,564 | 2,654 | 55,032 |
| 40 | 42,038 | 2,725 | 66,950 |
| 45 | 21,700 | 2,769 | 84,824 |
| 50 | 9,820 | 2,786 | 98,672 |
| 55 | 1,589 | 2,793 | 104,740 |
| 60 | 219 | 2,794 | 105,563 |
| 65 | 43 | 2,794 | 105,757 |
By gender
survfit.t5.gender<-survfit(Surv(time, status) ~ gender,
data = r.diagnosis.death)
ggsurvplot.event<-ggsurvplot(survfit.t5.gender, fun = "event", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.title = element_blank())
ggsurvplot.cloglog<-ggsurvplot(survfit.t5.gender, fun = "cloglog", conf.int =TRUE)
ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.position = "none")
ggsurv.t5.gender<- list(
ggsurvplot.event,
ggsurvplot.cloglog)
ggsurv.t5.gender <-arrange_ggsurvplots(ggsurv.t5.gender, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t5.gender.png",ggsurv.t5.gender,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t5.gender.png")
at.risk.t5.gender<-ggsurvplot(survfit.t5.gender, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
select(time, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t5.gender,
col.names = c("Time since diagnosis", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Time since diagnosis | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 0 | gender=Male | 44,565 | 0 | 0 |
| 0 | gender=Female | 63,993 | 0 | 0 |
| 5 | gender=Male | 41,060 | 147 | 3,998 |
| 5 | gender=Female | 60,633 | 207 | 3,848 |
| 10 | gender=Male | 36,965 | 387 | 7,517 |
| 10 | gender=Female | 55,664 | 554 | 8,153 |
| 15 | gender=Male | 34,632 | 635 | 9,898 |
| 15 | gender=Female | 52,149 | 909 | 11,917 |
| 20 | gender=Male | 31,995 | 831 | 12,452 |
| 20 | gender=Female | 47,493 | 1,194 | 16,506 |
| 25 | gender=Male | 28,740 | 955 | 15,159 |
| 25 | gender=Female | 42,176 | 1,389 | 20,950 |
| 30 | gender=Male | 25,654 | 1,045 | 19,032 |
| 30 | gender=Female | 37,016 | 1,481 | 27,347 |
| 35 | gender=Male | 22,249 | 1,100 | 22,263 |
| 35 | gender=Female | 31,315 | 1,554 | 32,769 |
| 40 | gender=Male | 17,693 | 1,133 | 27,079 |
| 40 | gender=Female | 24,345 | 1,592 | 39,871 |
| 45 | gender=Male | 9,102 | 1,160 | 34,599 |
| 45 | gender=Female | 12,598 | 1,609 | 50,225 |
| 50 | gender=Male | 4,068 | 1,165 | 40,518 |
| 50 | gender=Female | 5,752 | 1,621 | 58,154 |
| 55 | gender=Male | 594 | 1,169 | 43,018 |
| 55 | gender=Female | 995 | 1,624 | 61,722 |
| 60 | gender=Male | 65 | 1,169 | 43,338 |
| 60 | gender=Female | 154 | 1,625 | 62,225 |
| 65 | gender=Male | 7 | 1,169 | 43,395 |
| 65 | gender=Female | 36 | 1,625 | 62,362 |
By age
survfit.t5.age_gr<-survfit(Surv(time, status) ~ age_gr,
data = r.diagnosis.death)
ggsurv.t5.age_gr<- list(
ggsurvplot(survfit.t5.age_gr, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t5.age_gr, fun = "cloglog", conf.int =TRUE))
ggsurv.t5.age_gr <-arrange_ggsurvplots(ggsurv.t5.age_gr, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t5.age_gr.png",ggsurv.t5.age_gr,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t5.age_gr.png")
at.risk.t5.age_gr<-ggsurvplot(survfit.t5.age_gr, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
select(time, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t5.age_gr,
col.names = c("Time since diagnosis", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Time since diagnosis | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 0 | age_gr=Under 18 | 4,471 | 0 | 0 |
| 0 | age_gr=18 to 39 | 30,379 | 0 | 0 |
| 0 | age_gr=40 to 59 | 44,510 | 0 | 0 |
| 0 | age_gr=60 to 69 | 10,593 | 0 | 0 |
| 0 | age_gr=70 to 79 | 7,104 | 0 | 0 |
| 0 | age_gr=80 or older | 11,501 | 0 | 0 |
| 5 | age_gr=Under 18 | 4,236 | 0 | 250 |
| 5 | age_gr=18 to 39 | 29,363 | 1 | 1,175 |
| 5 | age_gr=40 to 59 | 42,300 | 11 | 2,685 |
| 5 | age_gr=60 to 69 | 9,558 | 18 | 1,218 |
| 5 | age_gr=70 to 79 | 5,999 | 55 | 1,257 |
| 5 | age_gr=80 or older | 10,237 | 269 | 1,261 |
| 10 | age_gr=Under 18 | 3,801 | 0 | 680 |
| 10 | age_gr=18 to 39 | 27,661 | 1 | 2,806 |
| 10 | age_gr=40 to 59 | 39,005 | 23 | 5,682 |
| 10 | age_gr=60 to 69 | 8,463 | 46 | 2,162 |
| 10 | age_gr=70 to 79 | 5,037 | 147 | 1,991 |
| 10 | age_gr=80 or older | 8,662 | 724 | 2,349 |
| 15 | age_gr=Under 18 | 3,534 | 0 | 1,011 |
| 15 | age_gr=18 to 39 | 26,382 | 1 | 4,397 |
| 15 | age_gr=40 to 59 | 37,150 | 31 | 7,849 |
| 15 | age_gr=60 to 69 | 7,928 | 72 | 2,717 |
| 15 | age_gr=70 to 79 | 4,532 | 236 | 2,442 |
| 15 | age_gr=80 or older | 7,255 | 1,204 | 3,399 |
| 20 | age_gr=Under 18 | 3,166 | 0 | 1,415 |
| 20 | age_gr=18 to 39 | 24,620 | 1 | 6,219 |
| 20 | age_gr=40 to 59 | 34,935 | 39 | 10,156 |
| 20 | age_gr=60 to 69 | 7,360 | 88 | 3,301 |
| 20 | age_gr=70 to 79 | 3,968 | 306 | 2,981 |
| 20 | age_gr=80 or older | 5,439 | 1,591 | 4,886 |
| 25 | age_gr=Under 18 | 2,715 | 0 | 1,799 |
| 25 | age_gr=18 to 39 | 22,379 | 1 | 8,222 |
| 25 | age_gr=40 to 59 | 31,882 | 46 | 12,848 |
| 25 | age_gr=60 to 69 | 6,653 | 103 | 3,891 |
| 25 | age_gr=70 to 79 | 3,375 | 347 | 3,434 |
| 25 | age_gr=80 or older | 3,912 | 1,847 | 5,915 |
| 30 | age_gr=Under 18 | 2,195 | 0 | 2,426 |
| 30 | age_gr=18 to 39 | 20,052 | 2 | 11,208 |
| 30 | age_gr=40 to 59 | 28,834 | 52 | 16,897 |
| 30 | age_gr=60 to 69 | 5,976 | 110 | 4,797 |
| 30 | age_gr=70 to 79 | 2,867 | 382 | 3,994 |
| 30 | age_gr=80 or older | 2,746 | 1,980 | 7,057 |
| 35 | age_gr=Under 18 | 1,748 | 0 | 2,883 |
| 35 | age_gr=18 to 39 | 17,309 | 2 | 13,909 |
| 35 | age_gr=40 to 59 | 25,129 | 55 | 20,417 |
| 35 | age_gr=60 to 69 | 5,157 | 120 | 5,583 |
| 35 | age_gr=70 to 79 | 2,343 | 400 | 4,490 |
| 35 | age_gr=80 or older | 1,878 | 2,077 | 7,750 |
| 40 | age_gr=Under 18 | 1,265 | 0 | 3,362 |
| 40 | age_gr=18 to 39 | 13,773 | 4 | 17,540 |
| 40 | age_gr=40 to 59 | 20,103 | 59 | 25,734 |
| 40 | age_gr=60 to 69 | 4,042 | 125 | 6,772 |
| 40 | age_gr=70 to 79 | 1,750 | 408 | 5,127 |
| 40 | age_gr=80 or older | 1,105 | 2,129 | 8,415 |
| 45 | age_gr=Under 18 | 559 | 0 | 3,931 |
| 45 | age_gr=18 to 39 | 7,315 | 5 | 23,290 |
| 45 | age_gr=40 to 59 | 10,717 | 62 | 34,078 |
| 45 | age_gr=60 to 69 | 1,982 | 129 | 8,552 |
| 45 | age_gr=70 to 79 | 751 | 415 | 5,974 |
| 45 | age_gr=80 or older | 376 | 2,158 | 8,999 |
| 50 | age_gr=Under 18 | 133 | 0 | 4,412 |
| 50 | age_gr=18 to 39 | 3,343 | 5 | 27,987 |
| 50 | age_gr=40 to 59 | 5,158 | 62 | 40,669 |
| 50 | age_gr=60 to 69 | 878 | 132 | 9,802 |
| 50 | age_gr=70 to 79 | 227 | 420 | 6,518 |
| 50 | age_gr=80 or older | 81 | 2,167 | 9,284 |
| 55 | age_gr=Under 18 | 19 | 0 | 4,457 |
| 55 | age_gr=18 to 39 | 526 | 5 | 30,053 |
| 55 | age_gr=40 to 59 | 817 | 63 | 43,923 |
| 55 | age_gr=60 to 69 | 158 | 135 | 10,339 |
| 55 | age_gr=70 to 79 | 51 | 421 | 6,648 |
| 55 | age_gr=80 or older | 18 | 2,169 | 9,320 |
| 60 | age_gr=Under 18 | 3 | 0 | 4,468 |
| 60 | age_gr=18 to 39 | 61 | 5 | 30,314 |
| 60 | age_gr=40 to 59 | 111 | 63 | 44,349 |
| 60 | age_gr=60 to 69 | 32 | 136 | 10,428 |
| 60 | age_gr=70 to 79 | 10 | 421 | 6,674 |
| 60 | age_gr=80 or older | 2 | 2,169 | 9,330 |
| 65 | age_gr=Under 18 | 1 | 0 | 4,471 |
| 65 | age_gr=18 to 39 | 16 | 5 | 30,371 |
| 65 | age_gr=40 to 59 | 23 | 63 | 44,443 |
| 65 | age_gr=60 to 69 | 1 | 136 | 10,457 |
| 65 | age_gr=70 to 79 | 2 | 421 | 6,683 |
| 65 | age_gr=80 or older | 0 | 2,169 | 9,332 |
By charlson
survfit.t5.charlson<-survfit(Surv(time, status) ~ charlson,
data = r.diagnosis.death)
ggsurv.t5.charlson<- list(
ggsurvplot(survfit.t5.charlson, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t5.charlson, fun = "cloglog", conf.int =TRUE))
ggsurv.t5.charlson <-arrange_ggsurvplots(ggsurv.t5.charlson, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t5.charlson.png",ggsurv.t5.charlson,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t5.charlson.png")
at.risk.t5.charlson<-ggsurvplot(survfit.t5.charlson, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
select(time, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t5.charlson,
col.names = c("Time since diagnosis", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Time since diagnosis | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 0 | charlson=0 | 81,206 | 0 | 0 |
| 0 | charlson=1 | 10,635 | 0 | 0 |
| 0 | charlson=2 | 8,161 | 0 | 0 |
| 0 | charlson=3+ | 8,556 | 0 | 0 |
| 5 | charlson=0 | 77,101 | 43 | 4,872 |
| 5 | charlson=1 | 9,781 | 81 | 931 |
| 5 | charlson=2 | 7,316 | 58 | 943 |
| 5 | charlson=3+ | 7,495 | 172 | 1,100 |
| 10 | charlson=0 | 71,161 | 123 | 10,274 |
| 10 | charlson=1 | 8,736 | 196 | 1,798 |
| 10 | charlson=2 | 6,408 | 167 | 1,679 |
| 10 | charlson=3+ | 6,324 | 455 | 1,919 |
| 15 | charlson=0 | 67,499 | 212 | 14,531 |
| 15 | charlson=1 | 8,034 | 318 | 2,466 |
| 15 | charlson=2 | 5,841 | 264 | 2,198 |
| 15 | charlson=3+ | 5,407 | 750 | 2,620 |
| 20 | charlson=0 | 62,883 | 275 | 19,275 |
| 20 | charlson=1 | 7,135 | 418 | 3,308 |
| 20 | charlson=2 | 5,141 | 353 | 2,860 |
| 20 | charlson=3+ | 4,329 | 979 | 3,515 |
| 25 | charlson=0 | 56,963 | 309 | 24,489 |
| 25 | charlson=1 | 6,186 | 483 | 4,045 |
| 25 | charlson=2 | 4,405 | 416 | 3,411 |
| 25 | charlson=3+ | 3,362 | 1,136 | 4,164 |
| 30 | charlson=0 | 50,935 | 341 | 32,262 |
| 30 | charlson=1 | 5,322 | 517 | 5,060 |
| 30 | charlson=2 | 3,770 | 457 | 4,130 |
| 30 | charlson=3+ | 2,643 | 1,211 | 4,927 |
| 35 | charlson=0 | 43,951 | 361 | 39,010 |
| 35 | charlson=1 | 4,502 | 544 | 5,820 |
| 35 | charlson=2 | 3,149 | 482 | 4,712 |
| 35 | charlson=3+ | 1,962 | 1,267 | 5,490 |
| 40 | charlson=0 | 34,822 | 375 | 48,525 |
| 40 | charlson=1 | 3,474 | 555 | 6,900 |
| 40 | charlson=2 | 2,413 | 495 | 5,445 |
| 40 | charlson=3+ | 1,329 | 1,300 | 6,080 |
| 45 | charlson=0 | 18,158 | 384 | 63,263 |
| 45 | charlson=1 | 1,757 | 563 | 8,369 |
| 45 | charlson=2 | 1,216 | 504 | 6,493 |
| 45 | charlson=3+ | 569 | 1,318 | 6,699 |
| 50 | charlson=0 | 8,297 | 389 | 74,856 |
| 50 | charlson=1 | 797 | 564 | 9,470 |
| 50 | charlson=2 | 508 | 506 | 7,279 |
| 50 | charlson=3+ | 218 | 1,327 | 7,067 |
| 55 | charlson=0 | 1,325 | 391 | 79,970 |
| 55 | charlson=1 | 128 | 565 | 9,980 |
| 55 | charlson=2 | 101 | 507 | 7,590 |
| 55 | charlson=3+ | 35 | 1,330 | 7,200 |
| 60 | charlson=0 | 186 | 391 | 80,644 |
| 60 | charlson=1 | 14 | 566 | 10,055 |
| 60 | charlson=2 | 16 | 507 | 7,641 |
| 60 | charlson=3+ | 3 | 1,330 | 7,223 |
| 65 | charlson=0 | 37 | 391 | 80,808 |
| 65 | charlson=1 | 3 | 566 | 10,069 |
| 65 | charlson=2 | 3 | 507 | 7,654 |
| 65 | charlson=3+ | 0 | 1,330 | 7,226 |
By a_autoimmune_condition
survfit.t5.a_autoimmune_condition<-survfit(Surv(time, status) ~ a_autoimmune_condition,
data = r.diagnosis.death)
ggsurv.t5.a_autoimmune_condition<- list(
ggsurvplot(survfit.t5.a_autoimmune_condition, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t5.a_autoimmune_condition, fun = "cloglog", conf.int =TRUE))
ggsurv.t5.a_autoimmune_condition <-arrange_ggsurvplots(ggsurv.t5.a_autoimmune_condition, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t5.a_autoimmune_condition.png",ggsurv.t5.a_autoimmune_condition,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t5.a_autoimmune_condition.png")
at.risk.t5.a_autoimmune_condition<-ggsurvplot(survfit.t5.a_autoimmune_condition, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
select(time, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t5.a_autoimmune_condition,
col.names = c("Time since diagnosis", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Time since diagnosis | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 0 | a_autoimmune_condition=0 | 101,670 | 0 | 0 |
| 0 | a_autoimmune_condition=1 | 6,888 | 0 | 0 |
| 5 | a_autoimmune_condition=0 | 95,345 | 314 | 7,224 |
| 5 | a_autoimmune_condition=1 | 6,348 | 40 | 622 |
| 10 | a_autoimmune_condition=0 | 86,932 | 839 | 14,533 |
| 10 | a_autoimmune_condition=1 | 5,697 | 102 | 1,137 |
| 15 | a_autoimmune_condition=0 | 81,472 | 1,379 | 20,283 |
| 15 | a_autoimmune_condition=1 | 5,309 | 165 | 1,532 |
| 20 | a_autoimmune_condition=0 | 74,696 | 1,808 | 26,923 |
| 20 | a_autoimmune_condition=1 | 4,792 | 217 | 2,035 |
| 25 | a_autoimmune_condition=0 | 66,728 | 2,091 | 33,612 |
| 25 | a_autoimmune_condition=1 | 4,188 | 253 | 2,497 |
| 30 | a_autoimmune_condition=0 | 58,975 | 2,255 | 43,288 |
| 30 | a_autoimmune_condition=1 | 3,695 | 271 | 3,091 |
| 35 | a_autoimmune_condition=0 | 50,434 | 2,368 | 51,416 |
| 35 | a_autoimmune_condition=1 | 3,130 | 286 | 3,616 |
| 40 | a_autoimmune_condition=0 | 39,577 | 2,439 | 62,639 |
| 40 | a_autoimmune_condition=1 | 2,461 | 286 | 4,311 |
| 45 | a_autoimmune_condition=0 | 20,389 | 2,480 | 79,485 |
| 45 | a_autoimmune_condition=1 | 1,311 | 289 | 5,339 |
| 50 | a_autoimmune_condition=0 | 9,233 | 2,495 | 92,500 |
| 50 | a_autoimmune_condition=1 | 587 | 291 | 6,172 |
| 55 | a_autoimmune_condition=0 | 1,493 | 2,502 | 98,202 |
| 55 | a_autoimmune_condition=1 | 96 | 291 | 6,538 |
| 60 | a_autoimmune_condition=0 | 204 | 2,503 | 98,981 |
| 60 | a_autoimmune_condition=1 | 15 | 291 | 6,582 |
| 65 | a_autoimmune_condition=0 | 43 | 2,503 | 99,160 |
| 65 | a_autoimmune_condition=1 | 0 | 291 | 6,597 |
By a_chronic_kidney_disease
survfit.t4.a_chronic_kidney_disease<-survfit(Surv(time, status) ~ a_chronic_kidney_disease,
data = r.diagnosis.hospitalised)
ggsurv.t4.a_chronic_kidney_disease<- list(
ggsurvplot(survfit.t4.a_chronic_kidney_disease, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t4.a_chronic_kidney_disease, fun = "cloglog", conf.int =TRUE))
ggsurv.t4.a_chronic_kidney_disease <-arrange_ggsurvplots(ggsurv.t4.a_chronic_kidney_disease, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t4.a_chronic_kidney_disease.png",ggsurv.t4.a_chronic_kidney_disease,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t4.a_chronic_kidney_disease.png")
at.risk.t4.a_chronic_kidney_disease<-ggsurvplot(survfit.t4.a_chronic_kidney_disease, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
select(time, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t4.a_chronic_kidney_disease,
col.names = c("Time since diagnosis", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Time since diagnosis | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 0 | a_chronic_kidney_disease=0 | 102,750 | 0 | 0 |
| 0 | a_chronic_kidney_disease=1 | 5,808 | 0 | 0 |
| 5 | a_chronic_kidney_disease=0 | 96,626 | 4,590 | 2,732 |
| 5 | a_chronic_kidney_disease=1 | 5,067 | 631 | 247 |
| 10 | a_chronic_kidney_disease=0 | 88,371 | 7,466 | 7,488 |
| 10 | a_chronic_kidney_disease=1 | 4,258 | 933 | 724 |
| 15 | a_chronic_kidney_disease=0 | 83,152 | 8,062 | 12,960 |
| 15 | a_chronic_kidney_disease=1 | 3,629 | 1,006 | 1,331 |
| 20 | a_chronic_kidney_disease=0 | 76,579 | 8,269 | 19,636 |
| 20 | a_chronic_kidney_disease=1 | 2,909 | 1,034 | 2,044 |
| 25 | a_chronic_kidney_disease=0 | 68,688 | 8,335 | 26,477 |
| 25 | a_chronic_kidney_disease=1 | 2,228 | 1,045 | 2,596 |
| 30 | a_chronic_kidney_disease=0 | 60,934 | 8,361 | 36,337 |
| 30 | a_chronic_kidney_disease=1 | 1,736 | 1,051 | 3,156 |
| 35 | a_chronic_kidney_disease=0 | 52,277 | 8,372 | 44,686 |
| 35 | a_chronic_kidney_disease=1 | 1,287 | 1,057 | 3,571 |
| 40 | a_chronic_kidney_disease=0 | 41,173 | 8,377 | 56,251 |
| 40 | a_chronic_kidney_disease=1 | 865 | 1,057 | 3,990 |
| 45 | a_chronic_kidney_disease=0 | 21,343 | 8,378 | 73,744 |
| 45 | a_chronic_kidney_disease=1 | 357 | 1,058 | 4,413 |
| 50 | a_chronic_kidney_disease=0 | 9,714 | 8,379 | 87,346 |
| 50 | a_chronic_kidney_disease=1 | 106 | 1,058 | 4,675 |
| 55 | a_chronic_kidney_disease=0 | 1,576 | 8,379 | 93,354 |
| 55 | a_chronic_kidney_disease=1 | 13 | 1,058 | 4,742 |
| 60 | a_chronic_kidney_disease=0 | 218 | 8,379 | 94,171 |
| 60 | a_chronic_kidney_disease=1 | 1 | 1,058 | 4,749 |
| 65 | a_chronic_kidney_disease=0 | 43 | 8,379 | 94,364 |
| 65 | a_chronic_kidney_disease=1 | 0 | 1,058 | 4,750 |
By a_copd
survfit.t4.a_copd<-survfit(Surv(time, status) ~ a_copd,
data = r.diagnosis.hospitalised)
ggsurv.t4.a_copd<- list(
ggsurvplot(survfit.t4.a_copd, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t4.a_copd, fun = "cloglog", conf.int =TRUE))
ggsurv.t4.a_copd <-arrange_ggsurvplots(ggsurv.t4.a_copd, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t4.a_copd.png",ggsurv.t4.a_copd,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t4.a_copd.png")
at.risk.t4.a_copd<-ggsurvplot(survfit.t4.a_copd, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
select(time, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t4.a_copd,
col.names = c("Time since diagnosis", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Time since diagnosis | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 0 | a_copd=0 | 105,705 | 0 | 0 |
| 0 | a_copd=1 | 2,853 | 0 | 0 |
| 5 | a_copd=0 | 99,229 | 4,897 | 2,855 |
| 5 | a_copd=1 | 2,464 | 324 | 124 |
| 10 | a_copd=0 | 90,517 | 7,934 | 7,901 |
| 10 | a_copd=1 | 2,112 | 465 | 311 |
| 15 | a_copd=0 | 84,909 | 8,572 | 13,750 |
| 15 | a_copd=1 | 1,872 | 496 | 541 |
| 20 | a_copd=0 | 77,887 | 8,794 | 20,843 |
| 20 | a_copd=1 | 1,601 | 509 | 837 |
| 25 | a_copd=0 | 69,597 | 8,867 | 28,025 |
| 25 | a_copd=1 | 1,319 | 513 | 1,048 |
| 30 | a_copd=0 | 61,559 | 8,897 | 38,195 |
| 30 | a_copd=1 | 1,111 | 515 | 1,298 |
| 35 | a_copd=0 | 52,678 | 8,914 | 46,746 |
| 35 | a_copd=1 | 886 | 515 | 1,511 |
| 40 | a_copd=0 | 41,383 | 8,919 | 58,487 |
| 40 | a_copd=1 | 655 | 515 | 1,754 |
| 45 | a_copd=0 | 21,377 | 8,921 | 76,134 |
| 45 | a_copd=1 | 323 | 515 | 2,023 |
| 50 | a_copd=0 | 9,683 | 8,922 | 89,790 |
| 50 | a_copd=1 | 137 | 515 | 2,231 |
| 55 | a_copd=0 | 1,566 | 8,922 | 95,771 |
| 55 | a_copd=1 | 23 | 515 | 2,325 |
| 60 | a_copd=0 | 216 | 8,922 | 96,585 |
| 60 | a_copd=1 | 3 | 515 | 2,335 |
| 65 | a_copd=0 | 42 | 8,922 | 96,776 |
| 65 | a_copd=1 | 1 | 515 | 2,338 |
By a_dementia
survfit.t4.a_dementia<-survfit(Surv(time, status) ~ a_dementia,
data = r.diagnosis.hospitalised)
ggsurv.t4.a_dementia<- list(
ggsurvplot(survfit.t4.a_dementia, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t4.a_dementia, fun = "cloglog", conf.int =TRUE))
ggsurv.t4.a_dementia <-arrange_ggsurvplots(ggsurv.t4.a_dementia, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t4.a_dementia.png",ggsurv.t4.a_dementia,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t4.a_dementia.png")
at.risk.t4.a_dementia<-ggsurvplot(survfit.t4.a_dementia, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
select(time, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t4.a_dementia,
col.names = c("Time since diagnosis", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Time since diagnosis | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 0 | a_dementia=0 | 103,783 | 0 | 0 |
| 0 | a_dementia=1 | 4,775 | 0 | 0 |
| 5 | a_dementia=0 | 97,359 | 4,961 | 2,704 |
| 5 | a_dementia=1 | 4,334 | 260 | 275 |
| 10 | a_dementia=0 | 88,891 | 8,010 | 7,471 |
| 10 | a_dementia=1 | 3,738 | 389 | 741 |
| 15 | a_dementia=0 | 83,667 | 8,621 | 12,921 |
| 15 | a_dementia=1 | 3,114 | 447 | 1,370 |
| 20 | a_dementia=0 | 77,279 | 8,830 | 19,398 |
| 20 | a_dementia=1 | 2,209 | 473 | 2,282 |
| 25 | a_dementia=0 | 69,456 | 8,898 | 26,152 |
| 25 | a_dementia=1 | 1,460 | 482 | 2,921 |
| 30 | a_dementia=0 | 61,733 | 8,928 | 36,024 |
| 30 | a_dementia=1 | 937 | 484 | 3,469 |
| 35 | a_dementia=0 | 52,992 | 8,944 | 44,472 |
| 35 | a_dementia=1 | 572 | 485 | 3,785 |
| 40 | a_dementia=0 | 41,754 | 8,948 | 56,193 |
| 40 | a_dementia=1 | 284 | 486 | 4,048 |
| 45 | a_dementia=0 | 21,617 | 8,950 | 73,941 |
| 45 | a_dementia=1 | 83 | 486 | 4,216 |
| 50 | a_dementia=0 | 9,806 | 8,951 | 87,741 |
| 50 | a_dementia=1 | 14 | 486 | 4,280 |
| 55 | a_dementia=0 | 1,585 | 8,951 | 93,811 |
| 55 | a_dementia=1 | 4 | 486 | 4,285 |
| 60 | a_dementia=0 | 219 | 8,951 | 94,631 |
| 60 | a_dementia=1 | 0 | 486 | 4,289 |
| 65 | a_dementia=0 | 43 | 8,951 | 94,825 |
| 65 | a_dementia=1 | 0 | 486 | 4,289 |
By a_heart_disease
survfit.t4.a_heart_disease<-survfit(Surv(time, status) ~ a_heart_disease,
data = r.diagnosis.hospitalised)
ggsurv.t4.a_heart_disease<- list(
ggsurvplot(survfit.t4.a_heart_disease, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t4.a_heart_disease, fun = "cloglog", conf.int =TRUE))
ggsurv.t4.a_heart_disease <-arrange_ggsurvplots(ggsurv.t4.a_heart_disease, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t4.a_heart_disease.png",ggsurv.t4.a_heart_disease,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t4.a_heart_disease.png")
at.risk.t4.a_heart_disease<-ggsurvplot(survfit.t4.a_heart_disease, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
select(time, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t4.a_heart_disease,
col.names = c("Time since diagnosis", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Time since diagnosis | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 0 | a_heart_disease=0 | 95,108 | 0 | 0 |
| 0 | a_heart_disease=1 | 13,450 | 0 | 0 |
| 5 | a_heart_disease=0 | 89,677 | 3,984 | 2,511 |
| 5 | a_heart_disease=1 | 12,016 | 1,237 | 468 |
| 10 | a_heart_disease=0 | 82,233 | 6,518 | 6,881 |
| 10 | a_heart_disease=1 | 10,396 | 1,881 | 1,331 |
| 15 | a_heart_disease=0 | 77,523 | 7,030 | 11,865 |
| 15 | a_heart_disease=1 | 9,258 | 2,038 | 2,426 |
| 20 | a_heart_disease=0 | 71,544 | 7,199 | 17,933 |
| 20 | a_heart_disease=1 | 7,944 | 2,104 | 3,747 |
| 25 | a_heart_disease=0 | 64,261 | 7,265 | 24,274 |
| 25 | a_heart_disease=1 | 6,655 | 2,115 | 4,799 |
| 30 | a_heart_disease=0 | 57,085 | 7,288 | 33,436 |
| 30 | a_heart_disease=1 | 5,585 | 2,124 | 6,057 |
| 35 | a_heart_disease=0 | 48,999 | 7,300 | 41,230 |
| 35 | a_heart_disease=1 | 4,565 | 2,129 | 7,027 |
| 40 | a_heart_disease=0 | 38,635 | 7,303 | 51,999 |
| 40 | a_heart_disease=1 | 3,403 | 2,131 | 8,242 |
| 45 | a_heart_disease=0 | 20,082 | 7,304 | 68,398 |
| 45 | a_heart_disease=1 | 1,618 | 2,132 | 9,759 |
| 50 | a_heart_disease=0 | 9,139 | 7,305 | 81,206 |
| 50 | a_heart_disease=1 | 681 | 2,132 | 10,815 |
| 55 | a_heart_disease=0 | 1,462 | 7,305 | 86,863 |
| 55 | a_heart_disease=1 | 127 | 2,132 | 11,233 |
| 60 | a_heart_disease=0 | 210 | 7,305 | 87,610 |
| 60 | a_heart_disease=1 | 9 | 2,132 | 11,310 |
| 65 | a_heart_disease=0 | 42 | 7,305 | 87,796 |
| 65 | a_heart_disease=1 | 1 | 2,132 | 11,318 |
By a_hyperlipidemia
survfit.t4.a_hyperlipidemia<-survfit(Surv(time, status) ~ a_hyperlipidemia,
data = r.diagnosis.hospitalised)
ggsurv.t4.a_hyperlipidemia<- list(
ggsurvplot(survfit.t4.a_hyperlipidemia, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t4.a_hyperlipidemia, fun = "cloglog", conf.int =TRUE))
ggsurv.t4.a_hyperlipidemia <-arrange_ggsurvplots(ggsurv.t4.a_hyperlipidemia, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t4.a_hyperlipidemia.png",ggsurv.t4.a_hyperlipidemia,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t4.a_hyperlipidemia.png")
at.risk.t4.a_hyperlipidemia<-ggsurvplot(survfit.t4.a_hyperlipidemia, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
select(time, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t4.a_hyperlipidemia,
col.names = c("Time since diagnosis", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Time since diagnosis | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 0 | a_hyperlipidemia=0 | 96,745 | 0 | 0 |
| 0 | a_hyperlipidemia=1 | 11,813 | 0 | 0 |
| 5 | a_hyperlipidemia=0 | 90,928 | 4,292 | 2,647 |
| 5 | a_hyperlipidemia=1 | 10,765 | 929 | 332 |
| 10 | a_hyperlipidemia=0 | 83,074 | 6,973 | 7,299 |
| 10 | a_hyperlipidemia=1 | 9,555 | 1,426 | 913 |
| 15 | a_hyperlipidemia=0 | 77,891 | 7,532 | 12,722 |
| 15 | a_hyperlipidemia=1 | 8,890 | 1,536 | 1,569 |
| 20 | a_hyperlipidemia=0 | 71,438 | 7,726 | 19,270 |
| 20 | a_hyperlipidemia=1 | 8,050 | 1,577 | 2,410 |
| 25 | a_hyperlipidemia=0 | 63,804 | 7,793 | 25,888 |
| 25 | a_hyperlipidemia=1 | 7,112 | 1,587 | 3,185 |
| 30 | a_hyperlipidemia=0 | 56,427 | 7,816 | 35,219 |
| 30 | a_hyperlipidemia=1 | 6,243 | 1,596 | 4,274 |
| 35 | a_hyperlipidemia=0 | 48,275 | 7,831 | 43,041 |
| 35 | a_hyperlipidemia=1 | 5,289 | 1,598 | 5,216 |
| 40 | a_hyperlipidemia=0 | 37,960 | 7,836 | 53,769 |
| 40 | a_hyperlipidemia=1 | 4,078 | 1,598 | 6,472 |
| 45 | a_hyperlipidemia=0 | 19,645 | 7,837 | 69,927 |
| 45 | a_hyperlipidemia=1 | 2,055 | 1,599 | 8,230 |
| 50 | a_hyperlipidemia=0 | 8,909 | 7,838 | 82,463 |
| 50 | a_hyperlipidemia=1 | 911 | 1,599 | 9,558 |
| 55 | a_hyperlipidemia=0 | 1,451 | 7,838 | 87,967 |
| 55 | a_hyperlipidemia=1 | 138 | 1,599 | 10,129 |
| 60 | a_hyperlipidemia=0 | 205 | 7,838 | 88,718 |
| 60 | a_hyperlipidemia=1 | 14 | 1,599 | 10,202 |
| 65 | a_hyperlipidemia=0 | 42 | 7,838 | 88,900 |
| 65 | a_hyperlipidemia=1 | 1 | 1,599 | 10,214 |
By a_hypertension
survfit.t4.a_hypertension<-survfit(Surv(time, status) ~ a_hypertension,
data = r.diagnosis.hospitalised)
ggsurv.t4.a_hypertension<- list(
ggsurvplot(survfit.t4.a_hypertension, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t4.a_hypertension, fun = "cloglog", conf.int =TRUE))
ggsurv.t4.a_hypertension <-arrange_ggsurvplots(ggsurv.t4.a_hypertension, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t4.a_hypertension.png",ggsurv.t4.a_hypertension,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t4.a_hypertension.png")
at.risk.t4.a_hypertension<-ggsurvplot(survfit.t4.a_hypertension, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
select(time, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t4.a_hypertension,
col.names = c("Time since diagnosis", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Time since diagnosis | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 0 | a_hypertension=0 | 92,240 | 0 | 0 |
| 0 | a_hypertension=1 | 16,318 | 0 | 0 |
| 5 | a_hypertension=0 | 86,936 | 3,832 | 2,472 |
| 5 | a_hypertension=1 | 14,757 | 1,389 | 507 |
| 10 | a_hypertension=0 | 79,660 | 6,263 | 6,831 |
| 10 | a_hypertension=1 | 12,969 | 2,136 | 1,381 |
| 15 | a_hypertension=0 | 74,974 | 6,756 | 11,784 |
| 15 | a_hypertension=1 | 11,807 | 2,312 | 2,507 |
| 20 | a_hypertension=0 | 69,146 | 6,932 | 17,705 |
| 20 | a_hypertension=1 | 10,342 | 2,371 | 3,975 |
| 25 | a_hypertension=0 | 62,063 | 6,986 | 23,877 |
| 25 | a_hypertension=1 | 8,853 | 2,394 | 5,196 |
| 30 | a_hypertension=0 | 55,092 | 7,008 | 32,786 |
| 30 | a_hypertension=1 | 7,578 | 2,404 | 6,707 |
| 35 | a_hypertension=0 | 47,187 | 7,018 | 40,394 |
| 35 | a_hypertension=1 | 6,377 | 2,411 | 7,863 |
| 40 | a_hypertension=0 | 37,157 | 7,022 | 50,833 |
| 40 | a_hypertension=1 | 4,881 | 2,412 | 9,408 |
| 45 | a_hypertension=0 | 19,258 | 7,024 | 66,623 |
| 45 | a_hypertension=1 | 2,442 | 2,412 | 11,534 |
| 50 | a_hypertension=0 | 8,743 | 7,025 | 78,895 |
| 50 | a_hypertension=1 | 1,077 | 2,412 | 13,126 |
| 55 | a_hypertension=0 | 1,418 | 7,025 | 84,313 |
| 55 | a_hypertension=1 | 171 | 2,412 | 13,783 |
| 60 | a_hypertension=0 | 192 | 7,025 | 85,039 |
| 60 | a_hypertension=1 | 27 | 2,412 | 13,881 |
| 65 | a_hypertension=0 | 40 | 7,025 | 85,208 |
| 65 | a_hypertension=1 | 3 | 2,412 | 13,906 |
By a_malignant_neoplasm
survfit.t4.a_malignant_neoplasm<-survfit(Surv(time, status) ~ a_malignant_neoplasm,
data = r.diagnosis.hospitalised)
ggsurv.t4.a_malignant_neoplasm<- list(
ggsurvplot(survfit.t4.a_malignant_neoplasm, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t4.a_malignant_neoplasm, fun = "cloglog", conf.int =TRUE))
ggsurv.t4.a_malignant_neoplasm <-arrange_ggsurvplots(ggsurv.t4.a_malignant_neoplasm, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t4.a_malignant_neoplasm.png",ggsurv.t4.a_malignant_neoplasm,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t4.a_malignant_neoplasm.png")
at.risk.t4.a_malignant_neoplasm<-ggsurvplot(survfit.t4.a_malignant_neoplasm, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
select(time, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t4.a_malignant_neoplasm,
col.names = c("Time since diagnosis", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Time since diagnosis | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 0 | a_malignant_neoplasm=0 | 101,936 | 0 | 0 |
| 0 | a_malignant_neoplasm=1 | 6,622 | 0 | 0 |
| 5 | a_malignant_neoplasm=0 | 95,773 | 4,584 | 2,764 |
| 5 | a_malignant_neoplasm=1 | 5,920 | 637 | 215 |
| 10 | a_malignant_neoplasm=0 | 87,509 | 7,434 | 7,595 |
| 10 | a_malignant_neoplasm=1 | 5,120 | 965 | 617 |
| 15 | a_malignant_neoplasm=0 | 82,151 | 8,039 | 13,187 |
| 15 | a_malignant_neoplasm=1 | 4,630 | 1,029 | 1,104 |
| 20 | a_malignant_neoplasm=0 | 75,465 | 8,244 | 19,994 |
| 20 | a_malignant_neoplasm=1 | 4,023 | 1,059 | 1,686 |
| 25 | a_malignant_neoplasm=0 | 67,455 | 8,315 | 26,921 |
| 25 | a_malignant_neoplasm=1 | 3,461 | 1,065 | 2,152 |
| 30 | a_malignant_neoplasm=0 | 59,706 | 8,342 | 36,721 |
| 30 | a_malignant_neoplasm=1 | 2,964 | 1,070 | 2,772 |
| 35 | a_malignant_neoplasm=0 | 51,144 | 8,355 | 44,985 |
| 35 | a_malignant_neoplasm=1 | 2,420 | 1,074 | 3,272 |
| 40 | a_malignant_neoplasm=0 | 40,198 | 8,360 | 56,373 |
| 40 | a_malignant_neoplasm=1 | 1,840 | 1,074 | 3,868 |
| 45 | a_malignant_neoplasm=0 | 20,784 | 8,361 | 73,482 |
| 45 | a_malignant_neoplasm=1 | 916 | 1,075 | 4,675 |
| 50 | a_malignant_neoplasm=0 | 9,416 | 8,362 | 86,779 |
| 50 | a_malignant_neoplasm=1 | 404 | 1,075 | 5,242 |
| 55 | a_malignant_neoplasm=0 | 1,511 | 8,362 | 92,597 |
| 55 | a_malignant_neoplasm=1 | 78 | 1,075 | 5,499 |
| 60 | a_malignant_neoplasm=0 | 207 | 8,362 | 93,384 |
| 60 | a_malignant_neoplasm=1 | 12 | 1,075 | 5,536 |
| 65 | a_malignant_neoplasm=0 | 40 | 8,362 | 93,567 |
| 65 | a_malignant_neoplasm=1 | 3 | 1,075 | 5,547 |
By a_obesity.5y
survfit.t4.a_obesity.5y<-survfit(Surv(time, status) ~ a_obesity.5y,
data = r.diagnosis.hospitalised)
ggsurv.t4.a_obesity.5y<- list(
ggsurvplot(survfit.t4.a_obesity.5y, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t4.a_obesity.5y, fun = "cloglog", conf.int =TRUE))
ggsurv.t4.a_obesity.5y <-arrange_ggsurvplots(ggsurv.t4.a_obesity.5y, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t4.a_obesity.5y.png",ggsurv.t4.a_obesity.5y,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t4.a_obesity.5y.png")
at.risk.t4.a_obesity.5y<-ggsurvplot(survfit.t4.a_obesity.5y, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
select(time, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t4.a_obesity.5y,
col.names = c("Time since diagnosis", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Time since diagnosis | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 0 | a_obesity.5y=0 | 86,219 | 0 | 0 |
| 0 | a_obesity.5y=1 | 22,339 | 0 | 0 |
| 5 | a_obesity.5y=0 | 81,448 | 3,293 | 2,385 |
| 5 | a_obesity.5y=1 | 20,245 | 1,928 | 594 |
| 10 | a_obesity.5y=0 | 74,764 | 5,427 | 6,535 |
| 10 | a_obesity.5y=1 | 17,865 | 2,972 | 1,677 |
| 15 | a_obesity.5y=0 | 70,210 | 5,898 | 11,372 |
| 15 | a_obesity.5y=1 | 16,571 | 3,170 | 2,919 |
| 20 | a_obesity.5y=0 | 64,437 | 6,060 | 17,201 |
| 20 | a_obesity.5y=1 | 15,051 | 3,243 | 4,479 |
| 25 | a_obesity.5y=0 | 57,672 | 6,112 | 23,104 |
| 25 | a_obesity.5y=1 | 13,244 | 3,268 | 5,969 |
| 30 | a_obesity.5y=0 | 51,036 | 6,132 | 31,485 |
| 30 | a_obesity.5y=1 | 11,634 | 3,280 | 8,008 |
| 35 | a_obesity.5y=0 | 43,740 | 6,145 | 38,520 |
| 35 | a_obesity.5y=1 | 9,824 | 3,284 | 9,737 |
| 40 | a_obesity.5y=0 | 34,461 | 6,149 | 48,095 |
| 40 | a_obesity.5y=1 | 7,577 | 3,285 | 12,146 |
| 45 | a_obesity.5y=0 | 17,987 | 6,151 | 62,679 |
| 45 | a_obesity.5y=1 | 3,713 | 3,285 | 15,478 |
| 50 | a_obesity.5y=0 | 8,286 | 6,151 | 74,068 |
| 50 | a_obesity.5y=1 | 1,534 | 3,286 | 17,953 |
| 55 | a_obesity.5y=0 | 1,328 | 6,151 | 79,213 |
| 55 | a_obesity.5y=1 | 261 | 3,286 | 18,883 |
| 60 | a_obesity.5y=0 | 178 | 6,151 | 79,906 |
| 60 | a_obesity.5y=1 | 41 | 3,286 | 19,014 |
| 65 | a_obesity.5y=0 | 35 | 6,151 | 80,063 |
| 65 | a_obesity.5y=1 | 8 | 3,286 | 19,051 |
By a_t2_diabetes
survfit.t4.a_t2_diabetes<-survfit(Surv(time, status) ~ a_t2_diabetes,
data = r.diagnosis.hospitalised)
ggsurv.t4.a_t2_diabetes<- list(
ggsurvplot(survfit.t4.a_t2_diabetes, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t4.a_t2_diabetes, fun = "cloglog", conf.int =TRUE))
ggsurv.t4.a_t2_diabetes <-arrange_ggsurvplots(ggsurv.t4.a_t2_diabetes, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t4.a_t2_diabetes.png",ggsurv.t4.a_t2_diabetes,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t4.a_t2_diabetes.png")
at.risk.t4.a_t2_diabetes<-ggsurvplot(survfit.t4.a_t2_diabetes, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
select(time, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t4.a_t2_diabetes,
col.names = c("Time since diagnosis", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Time since diagnosis | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 0 | a_t2_diabetes=0 | 101,182 | 0 | 0 |
| 0 | a_t2_diabetes=1 | 7,376 | 0 | 0 |
| 5 | a_t2_diabetes=0 | 95,248 | 4,377 | 2,719 |
| 5 | a_t2_diabetes=1 | 6,445 | 844 | 260 |
| 10 | a_t2_diabetes=0 | 87,154 | 7,126 | 7,504 |
| 10 | a_t2_diabetes=1 | 5,475 | 1,273 | 708 |
| 15 | a_t2_diabetes=0 | 81,891 | 7,701 | 13,019 |
| 15 | a_t2_diabetes=1 | 4,890 | 1,367 | 1,272 |
| 20 | a_t2_diabetes=0 | 75,297 | 7,902 | 19,715 |
| 20 | a_t2_diabetes=1 | 4,191 | 1,401 | 1,965 |
| 25 | a_t2_diabetes=0 | 67,413 | 7,971 | 26,535 |
| 25 | a_t2_diabetes=1 | 3,503 | 1,409 | 2,538 |
| 30 | a_t2_diabetes=0 | 59,718 | 7,996 | 36,312 |
| 30 | a_t2_diabetes=1 | 2,952 | 1,416 | 3,181 |
| 35 | a_t2_diabetes=0 | 51,144 | 8,009 | 44,560 |
| 35 | a_t2_diabetes=1 | 2,420 | 1,420 | 3,697 |
| 40 | a_t2_diabetes=0 | 40,250 | 8,012 | 55,916 |
| 40 | a_t2_diabetes=1 | 1,788 | 1,422 | 4,325 |
| 45 | a_t2_diabetes=0 | 20,890 | 8,014 | 72,975 |
| 45 | a_t2_diabetes=1 | 810 | 1,422 | 5,182 |
| 50 | a_t2_diabetes=0 | 9,501 | 8,015 | 86,305 |
| 50 | a_t2_diabetes=1 | 319 | 1,422 | 5,716 |
| 55 | a_t2_diabetes=0 | 1,531 | 8,015 | 92,182 |
| 55 | a_t2_diabetes=1 | 58 | 1,422 | 5,914 |
| 60 | a_t2_diabetes=0 | 215 | 8,015 | 92,970 |
| 60 | a_t2_diabetes=1 | 4 | 1,422 | 5,950 |
| 65 | a_t2_diabetes=0 | 43 | 8,015 | 93,160 |
| 65 | a_t2_diabetes=1 | 0 | 1,422 | 5,954 |
Transition 6: From hospitalised with COVID-19 to death
overall
survfit.t6<-survfit(Surv(time, status) ~ 1,
data = r.hospitalised.death)
ggsurvplot.event<-ggsurvplot(survfit.t6, fun = "event", palette="black")
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.position = "none")
ggsave( "ggsurv.t6.png",print(ggsurvplot.event),
dpi=300,
width = 6, height = 5)
include_graphics("ggsurv.t6.png")
survfit.t6<-survfit(Surv(time, status) ~ 1,
data = r.hospitalised.death)
ggsurvplot.event<-ggsurvplot(survfit.t6, fun = "event", conf.int =TRUE)
at.risk.t6<-ggsurvplot(survfit.t6, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
select(time, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t6,
col.names = c("Time since hospitalisation", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Time since hospitalisation | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|
| 0 | 17,976 | 0 | 0 |
| 5 | 17,658 | 182 | 241 |
| 10 | 16,868 | 638 | 634 |
| 15 | 15,836 | 1,208 | 1,186 |
| 20 | 14,654 | 1,649 | 1,968 |
| 25 | 13,291 | 1,957 | 2,976 |
| 30 | 11,599 | 2,215 | 4,651 |
| 35 | 9,563 | 2,396 | 6,536 |
| 40 | 6,731 | 2,566 | 9,458 |
| 45 | 3,030 | 2,687 | 12,653 |
| 50 | 969 | 2,771 | 14,501 |
| 55 | 242 | 2,784 | 15,016 |
| 60 | 70 | 2,788 | 15,128 |
| 65 | 11 | 2,791 | 15,185 |
By gender
survfit.t6.gender<-survfit(Surv(time, status) ~ gender,
data = r.hospitalised.death)
ggsurvplot.event<-ggsurvplot(survfit.t6.gender, fun = "event", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.title = element_blank())
ggsurvplot.cloglog<-ggsurvplot(survfit.t6.gender, fun = "cloglog", conf.int =TRUE)
ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.position = "none")
ggsurv.t6.gender<- list(
ggsurvplot.event,
ggsurvplot.cloglog)
ggsurv.t6.gender <-arrange_ggsurvplots(ggsurv.t6.gender, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t6.gender.png",ggsurv.t6.gender,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t6.gender.png")
at.risk.t6.gender<-ggsurvplot(survfit.t6.gender, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
select(time, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t6.gender,
col.names = c("Time since hospitalisation", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Time since hospitalisation | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 0 | gender=Male | 9,872 | 0 | 0 |
| 0 | gender=Female | 8,104 | 0 | 0 |
| 5 | gender=Male | 9,711 | 97 | 117 |
| 5 | gender=Female | 7,947 | 85 | 124 |
| 10 | gender=Male | 9,316 | 358 | 288 |
| 10 | gender=Female | 7,552 | 280 | 346 |
| 15 | gender=Male | 8,772 | 682 | 547 |
| 15 | gender=Female | 7,064 | 526 | 639 |
| 20 | gender=Male | 8,123 | 966 | 924 |
| 20 | gender=Female | 6,531 | 683 | 1,044 |
| 25 | gender=Male | 7,424 | 1,159 | 1,414 |
| 25 | gender=Female | 5,867 | 798 | 1,562 |
| 30 | gender=Male | 6,571 | 1,323 | 2,234 |
| 30 | gender=Female | 5,028 | 892 | 2,417 |
| 35 | gender=Male | 5,474 | 1,430 | 3,233 |
| 35 | gender=Female | 4,089 | 966 | 3,303 |
| 40 | gender=Male | 3,850 | 1,546 | 4,944 |
| 40 | gender=Female | 2,881 | 1,020 | 4,514 |
| 45 | gender=Male | 1,683 | 1,615 | 6,807 |
| 45 | gender=Female | 1,347 | 1,072 | 5,846 |
| 50 | gender=Male | 537 | 1,660 | 7,825 |
| 50 | gender=Female | 432 | 1,111 | 6,676 |
| 55 | gender=Male | 133 | 1,666 | 8,114 |
| 55 | gender=Female | 109 | 1,118 | 6,902 |
| 60 | gender=Male | 31 | 1,669 | 8,177 |
| 60 | gender=Female | 39 | 1,119 | 6,951 |
| 65 | gender=Male | 4 | 1,669 | 8,203 |
| 65 | gender=Female | 7 | 1,122 | 6,982 |
By age
survfit.t6.age_gr<-survfit(Surv(time, status) ~ age_gr,
data = r.hospitalised.death %>% filter(age_gr!="Under 18")) # count less than 5, so omit
ggsurv.t6.age_gr<- list(
ggsurvplot(survfit.t6.age_gr, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t6.age_gr, fun = "cloglog", conf.int =TRUE))
ggsurv.t6.age_gr <-arrange_ggsurvplots(ggsurv.t6.age_gr, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t6.age_gr.png",ggsurv.t6.age_gr,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t6.age_gr.png")
at.risk.t6.age_gr<-ggsurvplot(survfit.t6.age_gr, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
select(time, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t6.age_gr,
col.names = c("Time since hospitalisation", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Time since hospitalisation | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 0 | age_gr=18 to 39 | 1,283 | 0 | 0 |
| 0 | age_gr=40 to 59 | 5,271 | 0 | 0 |
| 0 | age_gr=60 to 69 | 3,297 | 0 | 0 |
| 0 | age_gr=70 to 79 | 3,949 | 0 | 0 |
| 0 | age_gr=80 or older | 4,103 | 0 | 0 |
| 5 | age_gr=18 to 39 | 1,267 | 0 | 20 |
| 5 | age_gr=40 to 59 | 5,221 | 13 | 52 |
| 5 | age_gr=60 to 69 | 3,264 | 8 | 34 |
| 5 | age_gr=70 to 79 | 3,897 | 40 | 32 |
| 5 | age_gr=80 or older | 3,939 | 121 | 99 |
| 10 | age_gr=18 to 39 | 1,225 | 2 | 62 |
| 10 | age_gr=40 to 59 | 5,121 | 22 | 144 |
| 10 | age_gr=60 to 69 | 3,193 | 40 | 83 |
| 10 | age_gr=70 to 79 | 3,734 | 155 | 105 |
| 10 | age_gr=80 or older | 3,532 | 419 | 230 |
| 15 | age_gr=18 to 39 | 1,171 | 3 | 126 |
| 15 | age_gr=40 to 59 | 4,971 | 48 | 293 |
| 15 | age_gr=60 to 69 | 3,089 | 86 | 144 |
| 15 | age_gr=70 to 79 | 3,488 | 317 | 200 |
| 15 | age_gr=80 or older | 3,057 | 754 | 410 |
| 20 | age_gr=18 to 39 | 1,099 | 5 | 194 |
| 20 | age_gr=40 to 59 | 4,749 | 71 | 521 |
| 20 | age_gr=60 to 69 | 2,965 | 128 | 259 |
| 20 | age_gr=70 to 79 | 3,232 | 466 | 317 |
| 20 | age_gr=80 or older | 2,556 | 979 | 657 |
| 25 | age_gr=18 to 39 | 1,010 | 6 | 285 |
| 25 | age_gr=40 to 59 | 4,396 | 92 | 842 |
| 25 | age_gr=60 to 69 | 2,767 | 169 | 403 |
| 25 | age_gr=70 to 79 | 2,961 | 569 | 471 |
| 25 | age_gr=80 or older | 2,110 | 1,120 | 948 |
| 30 | age_gr=18 to 39 | 880 | 8 | 443 |
| 30 | age_gr=40 to 59 | 3,874 | 107 | 1,471 |
| 30 | age_gr=60 to 69 | 2,490 | 209 | 686 |
| 30 | age_gr=70 to 79 | 2,644 | 655 | 733 |
| 30 | age_gr=80 or older | 1,675 | 1,235 | 1,281 |
| 35 | age_gr=18 to 39 | 710 | 8 | 598 |
| 35 | age_gr=40 to 59 | 3,134 | 123 | 2,208 |
| 35 | age_gr=60 to 69 | 2,090 | 240 | 1,068 |
| 35 | age_gr=70 to 79 | 2,283 | 698 | 1,072 |
| 35 | age_gr=80 or older | 1,317 | 1,326 | 1,544 |
| 40 | age_gr=18 to 39 | 492 | 9 | 822 |
| 40 | age_gr=40 to 59 | 2,152 | 133 | 3,249 |
| 40 | age_gr=60 to 69 | 1,533 | 269 | 1,679 |
| 40 | age_gr=70 to 79 | 1,650 | 761 | 1,737 |
| 40 | age_gr=80 or older | 882 | 1,393 | 1,920 |
| 45 | age_gr=18 to 39 | 239 | 10 | 1,066 |
| 45 | age_gr=40 to 59 | 935 | 140 | 4,333 |
| 45 | age_gr=60 to 69 | 697 | 283 | 2,412 |
| 45 | age_gr=70 to 79 | 724 | 798 | 2,508 |
| 45 | age_gr=80 or older | 423 | 1,455 | 2,273 |
| 50 | age_gr=18 to 39 | 82 | 11 | 1,213 |
| 50 | age_gr=40 to 59 | 277 | 141 | 4,930 |
| 50 | age_gr=60 to 69 | 199 | 294 | 2,852 |
| 50 | age_gr=70 to 79 | 247 | 834 | 2,944 |
| 50 | age_gr=80 or older | 158 | 1,490 | 2,496 |
| 55 | age_gr=18 to 39 | 17 | 11 | 1,259 |
| 55 | age_gr=40 to 59 | 67 | 142 | 5,078 |
| 55 | age_gr=60 to 69 | 49 | 296 | 2,972 |
| 55 | age_gr=70 to 79 | 55 | 838 | 3,069 |
| 55 | age_gr=80 or older | 52 | 1,496 | 2,566 |
| 60 | age_gr=18 to 39 | 5 | 11 | 1,267 |
| 60 | age_gr=40 to 59 | 22 | 143 | 5,110 |
| 60 | age_gr=60 to 69 | 9 | 296 | 2,993 |
| 60 | age_gr=70 to 79 | 16 | 840 | 3,095 |
| 60 | age_gr=80 or older | 18 | 1,497 | 2,591 |
| 65 | age_gr=18 to 39 | 2 | 11 | 1,272 |
| 65 | age_gr=40 to 59 | 4 | 143 | 5,128 |
| 65 | age_gr=60 to 69 | 0 | 296 | 3,001 |
| 65 | age_gr=70 to 79 | 3 | 840 | 3,109 |
| 65 | age_gr=80 or older | 2 | 1,500 | 2,603 |
By charlson
survfit.t6.charlson<-survfit(Surv(time, status) ~ charlson,
data = r.hospitalised.death)
ggsurv.t6.charlson<- list(
ggsurvplot(survfit.t6.charlson, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t6.charlson, fun = "cloglog", conf.int =TRUE))
ggsurv.t6.charlson <-arrange_ggsurvplots(ggsurv.t6.charlson, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t6.charlson.png",ggsurv.t6.charlson,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t6.charlson.png")
at.risk.t6.charlson<-ggsurvplot(survfit.t6.charlson, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
select(time, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t6.charlson,
col.names = c("Time since hospitalisation", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Time since hospitalisation | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 0 | charlson=0 | 9,233 | 0 | 0 |
| 0 | charlson=1 | 2,310 | 0 | 0 |
| 0 | charlson=2 | 2,779 | 0 | 0 |
| 0 | charlson=3+ | 3,654 | 0 | 0 |
| 5 | charlson=0 | 9,132 | 35 | 96 |
| 5 | charlson=1 | 2,256 | 32 | 41 |
| 5 | charlson=2 | 2,723 | 36 | 33 |
| 5 | charlson=3+ | 3,547 | 79 | 71 |
| 10 | charlson=0 | 8,899 | 124 | 263 |
| 10 | charlson=1 | 2,135 | 95 | 103 |
| 10 | charlson=2 | 2,573 | 138 | 102 |
| 10 | charlson=3+ | 3,261 | 281 | 166 |
| 15 | charlson=0 | 8,566 | 228 | 524 |
| 15 | charlson=1 | 1,971 | 194 | 180 |
| 15 | charlson=2 | 2,393 | 258 | 176 |
| 15 | charlson=3+ | 2,906 | 528 | 306 |
| 20 | charlson=0 | 8,154 | 311 | 890 |
| 20 | charlson=1 | 1,798 | 275 | 281 |
| 20 | charlson=2 | 2,188 | 349 | 291 |
| 20 | charlson=3+ | 2,514 | 714 | 506 |
| 25 | charlson=0 | 7,561 | 385 | 1,394 |
| 25 | charlson=1 | 1,635 | 315 | 401 |
| 25 | charlson=2 | 1,966 | 406 | 442 |
| 25 | charlson=3+ | 2,129 | 851 | 739 |
| 30 | charlson=0 | 6,713 | 457 | 2,338 |
| 30 | charlson=1 | 1,390 | 361 | 616 |
| 30 | charlson=2 | 1,716 | 445 | 687 |
| 30 | charlson=3+ | 1,780 | 952 | 1,010 |
| 35 | charlson=0 | 5,567 | 491 | 3,489 |
| 35 | charlson=1 | 1,165 | 392 | 813 |
| 35 | charlson=2 | 1,411 | 487 | 952 |
| 35 | charlson=3+ | 1,420 | 1,026 | 1,282 |
| 40 | charlson=0 | 3,912 | 539 | 5,255 |
| 40 | charlson=1 | 837 | 414 | 1,149 |
| 40 | charlson=2 | 982 | 521 | 1,379 |
| 40 | charlson=3+ | 1,000 | 1,092 | 1,675 |
| 45 | charlson=0 | 1,720 | 577 | 7,182 |
| 45 | charlson=1 | 369 | 433 | 1,554 |
| 45 | charlson=2 | 466 | 545 | 1,822 |
| 45 | charlson=3+ | 475 | 1,132 | 2,095 |
| 50 | charlson=0 | 480 | 601 | 8,292 |
| 50 | charlson=1 | 142 | 444 | 1,761 |
| 50 | charlson=2 | 164 | 565 | 2,092 |
| 50 | charlson=3+ | 183 | 1,161 | 2,356 |
| 55 | charlson=0 | 100 | 603 | 8,563 |
| 55 | charlson=1 | 39 | 444 | 1,838 |
| 55 | charlson=2 | 46 | 571 | 2,176 |
| 55 | charlson=3+ | 57 | 1,166 | 2,439 |
| 60 | charlson=0 | 26 | 604 | 8,606 |
| 60 | charlson=1 | 13 | 445 | 1,852 |
| 60 | charlson=2 | 10 | 572 | 2,199 |
| 60 | charlson=3+ | 21 | 1,167 | 2,471 |
| 65 | charlson=0 | 4 | 604 | 8,629 |
| 65 | charlson=1 | 2 | 447 | 1,863 |
| 65 | charlson=2 | 3 | 572 | 2,207 |
| 65 | charlson=3+ | 2 | 1,168 | 2,486 |
By a_autoimmune_condition
survfit.t6.a_autoimmune_condition<-survfit(Surv(time, status) ~ a_autoimmune_condition,
data = r.hospitalised.death)
ggsurv.t6.a_autoimmune_condition<- list(
ggsurvplot(survfit.t6.a_autoimmune_condition, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t6.a_autoimmune_condition, fun = "cloglog", conf.int =TRUE))
ggsurv.t6.a_autoimmune_condition <-arrange_ggsurvplots(ggsurv.t6.a_autoimmune_condition, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t6.a_autoimmune_condition.png",ggsurv.t6.a_autoimmune_condition,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t6.a_autoimmune_condition.png")
at.risk.t6.a_autoimmune_condition<-ggsurvplot(survfit.t6.a_autoimmune_condition, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
select(time, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t6.a_autoimmune_condition,
col.names = c("Time since hospitalisation", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Time since hospitalisation | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 0 | a_autoimmune_condition=0 | 16,323 | 0 | 0 |
| 0 | a_autoimmune_condition=1 | 1,653 | 0 | 0 |
| 5 | a_autoimmune_condition=0 | 16,031 | 170 | 223 |
| 5 | a_autoimmune_condition=1 | 1,627 | 12 | 18 |
| 10 | a_autoimmune_condition=0 | 15,317 | 579 | 583 |
| 10 | a_autoimmune_condition=1 | 1,551 | 59 | 51 |
| 15 | a_autoimmune_condition=0 | 14,392 | 1,068 | 1,085 |
| 15 | a_autoimmune_condition=1 | 1,444 | 140 | 101 |
| 20 | a_autoimmune_condition=0 | 13,360 | 1,437 | 1,788 |
| 20 | a_autoimmune_condition=1 | 1,294 | 212 | 180 |
| 25 | a_autoimmune_condition=0 | 12,140 | 1,710 | 2,703 |
| 25 | a_autoimmune_condition=1 | 1,151 | 247 | 273 |
| 30 | a_autoimmune_condition=0 | 10,598 | 1,936 | 4,238 |
| 30 | a_autoimmune_condition=1 | 1,001 | 279 | 413 |
| 35 | a_autoimmune_condition=0 | 8,749 | 2,097 | 5,954 |
| 35 | a_autoimmune_condition=1 | 814 | 299 | 582 |
| 40 | a_autoimmune_condition=0 | 6,173 | 2,253 | 8,625 |
| 40 | a_autoimmune_condition=1 | 558 | 313 | 833 |
| 45 | a_autoimmune_condition=0 | 2,765 | 2,359 | 11,562 |
| 45 | a_autoimmune_condition=1 | 265 | 328 | 1,091 |
| 50 | a_autoimmune_condition=0 | 879 | 2,434 | 13,247 |
| 50 | a_autoimmune_condition=1 | 90 | 337 | 1,254 |
| 55 | a_autoimmune_condition=0 | 213 | 2,446 | 13,724 |
| 55 | a_autoimmune_condition=1 | 29 | 338 | 1,292 |
| 60 | a_autoimmune_condition=0 | 59 | 2,449 | 13,824 |
| 60 | a_autoimmune_condition=1 | 11 | 339 | 1,304 |
| 65 | a_autoimmune_condition=0 | 9 | 2,451 | 13,872 |
| 65 | a_autoimmune_condition=1 | 2 | 340 | 1,313 |
By a_chronic_kidney_disease
survfit.t6.a_chronic_kidney_disease<-survfit(Surv(time, status) ~ a_chronic_kidney_disease,
data = r.hospitalised.death)
ggsurv.t6.a_chronic_kidney_disease<- list(
ggsurvplot(survfit.t6.a_chronic_kidney_disease, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t6.a_chronic_kidney_disease, fun = "cloglog", conf.int =TRUE))
ggsurv.t6.a_chronic_kidney_disease <-arrange_ggsurvplots(ggsurv.t6.a_chronic_kidney_disease, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t6.a_chronic_kidney_disease.png",ggsurv.t6.a_chronic_kidney_disease,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t6.a_chronic_kidney_disease.png")
at.risk.t6.a_chronic_kidney_disease<-ggsurvplot(survfit.t6.a_chronic_kidney_disease, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
select(time, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t6.a_chronic_kidney_disease,
col.names = c("Time since hospitalisation", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Time since hospitalisation | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 0 | a_chronic_kidney_disease=0 | 15,377 | 0 | 0 |
| 0 | a_chronic_kidney_disease=1 | 2,599 | 0 | 0 |
| 5 | a_chronic_kidney_disease=0 | 15,129 | 136 | 192 |
| 5 | a_chronic_kidney_disease=1 | 2,529 | 46 | 49 |
| 10 | a_chronic_kidney_disease=0 | 14,540 | 448 | 521 |
| 10 | a_chronic_kidney_disease=1 | 2,328 | 190 | 113 |
| 15 | a_chronic_kidney_disease=0 | 13,750 | 832 | 979 |
| 15 | a_chronic_kidney_disease=1 | 2,086 | 376 | 207 |
| 20 | a_chronic_kidney_disease=0 | 12,863 | 1,128 | 1,632 |
| 20 | a_chronic_kidney_disease=1 | 1,791 | 521 | 336 |
| 25 | a_chronic_kidney_disease=0 | 11,757 | 1,338 | 2,497 |
| 25 | a_chronic_kidney_disease=1 | 1,534 | 619 | 479 |
| 30 | a_chronic_kidney_disease=0 | 10,311 | 1,524 | 3,971 |
| 30 | a_chronic_kidney_disease=1 | 1,288 | 691 | 680 |
| 35 | a_chronic_kidney_disease=0 | 8,520 | 1,657 | 5,655 |
| 35 | a_chronic_kidney_disease=1 | 1,043 | 739 | 881 |
| 40 | a_chronic_kidney_disease=0 | 6,043 | 1,770 | 8,268 |
| 40 | a_chronic_kidney_disease=1 | 688 | 796 | 1,190 |
| 45 | a_chronic_kidney_disease=0 | 2,703 | 1,860 | 11,180 |
| 45 | a_chronic_kidney_disease=1 | 327 | 827 | 1,473 |
| 50 | a_chronic_kidney_disease=0 | 846 | 1,921 | 12,842 |
| 50 | a_chronic_kidney_disease=1 | 123 | 850 | 1,659 |
| 55 | a_chronic_kidney_disease=0 | 204 | 1,928 | 13,305 |
| 55 | a_chronic_kidney_disease=1 | 38 | 856 | 1,711 |
| 60 | a_chronic_kidney_disease=0 | 58 | 1,931 | 13,396 |
| 60 | a_chronic_kidney_disease=1 | 12 | 857 | 1,732 |
| 65 | a_chronic_kidney_disease=0 | 11 | 1,933 | 13,444 |
| 65 | a_chronic_kidney_disease=1 | 0 | 858 | 1,741 |
By a_copd
survfit.t6.a_copd<-survfit(Surv(time, status) ~ a_copd,
data = r.hospitalised.death)
ggsurv.t6.a_copd<- list(
ggsurvplot(survfit.t6.a_copd, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t6.a_copd, fun = "cloglog", conf.int =TRUE))
ggsurv.t6.a_copd <-arrange_ggsurvplots(ggsurv.t6.a_copd, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t6.a_copd.png",ggsurv.t6.a_copd,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t6.a_copd.png")
at.risk.t6.a_copd<-ggsurvplot(survfit.t6.a_copd, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
select(time, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t6.a_copd,
col.names = c("Time since hospitalisation", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Time since hospitalisation | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 0 | a_copd=0 | 16,663 | 0 | 0 |
| 0 | a_copd=1 | 1,313 | 0 | 0 |
| 5 | a_copd=0 | 16,370 | 166 | 222 |
| 5 | a_copd=1 | 1,288 | 16 | 19 |
| 10 | a_copd=0 | 15,662 | 562 | 579 |
| 10 | a_copd=1 | 1,206 | 76 | 55 |
| 15 | a_copd=0 | 14,753 | 1,055 | 1,083 |
| 15 | a_copd=1 | 1,083 | 153 | 103 |
| 20 | a_copd=0 | 13,705 | 1,426 | 1,800 |
| 20 | a_copd=1 | 949 | 223 | 168 |
| 25 | a_copd=0 | 12,471 | 1,688 | 2,726 |
| 25 | a_copd=1 | 820 | 269 | 250 |
| 30 | a_copd=0 | 10,905 | 1,903 | 4,311 |
| 30 | a_copd=1 | 694 | 312 | 340 |
| 35 | a_copd=0 | 8,980 | 2,064 | 6,110 |
| 35 | a_copd=1 | 583 | 332 | 426 |
| 40 | a_copd=0 | 6,313 | 2,211 | 8,882 |
| 40 | a_copd=1 | 418 | 355 | 576 |
| 45 | a_copd=0 | 2,815 | 2,319 | 11,907 |
| 45 | a_copd=1 | 215 | 368 | 746 |
| 50 | a_copd=0 | 877 | 2,394 | 13,637 |
| 50 | a_copd=1 | 92 | 377 | 864 |
| 55 | a_copd=0 | 218 | 2,406 | 14,100 |
| 55 | a_copd=1 | 24 | 378 | 916 |
| 60 | a_copd=0 | 60 | 2,409 | 14,203 |
| 60 | a_copd=1 | 10 | 379 | 925 |
| 65 | a_copd=0 | 9 | 2,412 | 14,251 |
| 65 | a_copd=1 | 2 | 379 | 934 |
By a_dementia
survfit.t6.a_dementia<-survfit(Surv(time, status) ~ a_dementia,
data = r.hospitalised.death)
ggsurv.t6.a_dementia<- list(
ggsurvplot(survfit.t6.a_dementia, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t6.a_dementia, fun = "cloglog", conf.int =TRUE))
ggsurv.t6.a_dementia <-arrange_ggsurvplots(ggsurv.t6.a_dementia, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t6.a_dementia.png",ggsurv.t6.a_dementia,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t6.a_dementia.png")
at.risk.t6.a_dementia<-ggsurvplot(survfit.t6.a_dementia, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
select(time, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t6.a_dementia,
col.names = c("Time since hospitalisation", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Time since hospitalisation | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 0 | a_dementia=0 | 16,893 | 0 | 0 |
| 0 | a_dementia=1 | 1,083 | 0 | 0 |
| 5 | a_dementia=0 | 16,633 | 147 | 198 |
| 5 | a_dementia=1 | 1,025 | 35 | 43 |
| 10 | a_dementia=0 | 15,969 | 514 | 545 |
| 10 | a_dementia=1 | 899 | 124 | 89 |
| 15 | a_dementia=0 | 15,103 | 988 | 1,022 |
| 15 | a_dementia=1 | 733 | 220 | 164 |
| 20 | a_dementia=0 | 14,064 | 1,373 | 1,719 |
| 20 | a_dementia=1 | 590 | 276 | 249 |
| 25 | a_dementia=0 | 12,840 | 1,638 | 2,638 |
| 25 | a_dementia=1 | 451 | 319 | 338 |
| 30 | a_dementia=0 | 11,282 | 1,861 | 4,211 |
| 30 | a_dementia=1 | 317 | 354 | 440 |
| 35 | a_dementia=0 | 9,342 | 2,018 | 6,032 |
| 35 | a_dementia=1 | 221 | 378 | 504 |
| 40 | a_dementia=0 | 6,591 | 2,170 | 8,893 |
| 40 | a_dementia=1 | 140 | 396 | 565 |
| 45 | a_dementia=0 | 2,952 | 2,282 | 12,047 |
| 45 | a_dementia=1 | 78 | 405 | 606 |
| 50 | a_dementia=0 | 944 | 2,355 | 13,852 |
| 50 | a_dementia=1 | 25 | 416 | 649 |
| 55 | a_dementia=0 | 234 | 2,368 | 14,357 |
| 55 | a_dementia=1 | 8 | 416 | 659 |
| 60 | a_dementia=0 | 68 | 2,372 | 14,463 |
| 60 | a_dementia=1 | 2 | 416 | 665 |
| 65 | a_dementia=0 | 10 | 2,375 | 14,518 |
| 65 | a_dementia=1 | 1 | 416 | 667 |
By a_heart_disease
survfit.t6.a_heart_disease<-survfit(Surv(time, status) ~ a_heart_disease,
data = r.hospitalised.death)
ggsurv.t6.a_heart_disease<- list(
ggsurvplot(survfit.t6.a_heart_disease, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t6.a_heart_disease, fun = "cloglog", conf.int =TRUE))
ggsurv.t6.a_heart_disease <-arrange_ggsurvplots(ggsurv.t6.a_heart_disease, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t6.a_heart_disease.png",ggsurv.t6.a_heart_disease,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t6.a_heart_disease.png")
at.risk.t6.a_heart_disease<-ggsurvplot(survfit.t6.a_heart_disease, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
select(time, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t6.a_heart_disease,
col.names = c("Time since hospitalisation", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Time since hospitalisation | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 0 | a_heart_disease=0 | 13,006 | 0 | 0 |
| 0 | a_heart_disease=1 | 4,970 | 0 | 0 |
| 5 | a_heart_disease=0 | 12,824 | 89 | 151 |
| 5 | a_heart_disease=1 | 4,834 | 93 | 90 |
| 10 | a_heart_disease=0 | 12,372 | 299 | 429 |
| 10 | a_heart_disease=1 | 4,496 | 339 | 205 |
| 15 | a_heart_disease=0 | 11,748 | 572 | 828 |
| 15 | a_heart_disease=1 | 4,088 | 636 | 358 |
| 20 | a_heart_disease=0 | 11,027 | 792 | 1,381 |
| 20 | a_heart_disease=1 | 3,627 | 857 | 587 |
| 25 | a_heart_disease=0 | 10,120 | 952 | 2,099 |
| 25 | a_heart_disease=1 | 3,171 | 1,005 | 877 |
| 30 | a_heart_disease=0 | 8,917 | 1,098 | 3,373 |
| 30 | a_heart_disease=1 | 2,682 | 1,117 | 1,278 |
| 35 | a_heart_disease=0 | 7,357 | 1,199 | 4,856 |
| 35 | a_heart_disease=1 | 2,206 | 1,197 | 1,680 |
| 40 | a_heart_disease=0 | 5,191 | 1,295 | 7,153 |
| 40 | a_heart_disease=1 | 1,540 | 1,271 | 2,305 |
| 45 | a_heart_disease=0 | 2,303 | 1,367 | 9,648 |
| 45 | a_heart_disease=1 | 727 | 1,320 | 3,005 |
| 50 | a_heart_disease=0 | 723 | 1,409 | 11,077 |
| 50 | a_heart_disease=1 | 246 | 1,362 | 3,424 |
| 55 | a_heart_disease=0 | 170 | 1,417 | 11,471 |
| 55 | a_heart_disease=1 | 72 | 1,367 | 3,545 |
| 60 | a_heart_disease=0 | 47 | 1,419 | 11,546 |
| 60 | a_heart_disease=1 | 23 | 1,369 | 3,582 |
| 65 | a_heart_disease=0 | 9 | 1,420 | 11,586 |
| 65 | a_heart_disease=1 | 2 | 1,371 | 3,599 |
By a_hyperlipidemia
survfit.t6.a_hyperlipidemia<-survfit(Surv(time, status) ~ a_hyperlipidemia,
data = r.hospitalised.death)
ggsurv.t6.a_hyperlipidemia<- list(
ggsurvplot(survfit.t6.a_hyperlipidemia, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t6.a_hyperlipidemia, fun = "cloglog", conf.int =TRUE))
ggsurv.t6.a_hyperlipidemia <-arrange_ggsurvplots(ggsurv.t6.a_hyperlipidemia, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t6.a_hyperlipidemia.png",ggsurv.t6.a_hyperlipidemia,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t6.a_hyperlipidemia.png")
at.risk.t6.a_hyperlipidemia<-ggsurvplot(survfit.t6.a_hyperlipidemia, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
select(time, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t6.a_hyperlipidemia,
col.names = c("Time since hospitalisation", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Time since hospitalisation | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 0 | a_hyperlipidemia=0 | 14,793 | 0 | 0 |
| 0 | a_hyperlipidemia=1 | 3,183 | 0 | 0 |
| 5 | a_hyperlipidemia=0 | 14,525 | 150 | 203 |
| 5 | a_hyperlipidemia=1 | 3,133 | 32 | 38 |
| 10 | a_hyperlipidemia=0 | 13,849 | 541 | 546 |
| 10 | a_hyperlipidemia=1 | 3,019 | 97 | 88 |
| 15 | a_hyperlipidemia=0 | 12,991 | 1,012 | 1,001 |
| 15 | a_hyperlipidemia=1 | 2,845 | 196 | 185 |
| 20 | a_hyperlipidemia=0 | 12,005 | 1,359 | 1,661 |
| 20 | a_hyperlipidemia=1 | 2,649 | 290 | 307 |
| 25 | a_hyperlipidemia=0 | 10,872 | 1,621 | 2,520 |
| 25 | a_hyperlipidemia=1 | 2,419 | 336 | 456 |
| 30 | a_hyperlipidemia=0 | 9,462 | 1,827 | 3,905 |
| 30 | a_hyperlipidemia=1 | 2,137 | 388 | 746 |
| 35 | a_hyperlipidemia=0 | 7,783 | 1,968 | 5,471 |
| 35 | a_hyperlipidemia=1 | 1,780 | 428 | 1,065 |
| 40 | a_hyperlipidemia=0 | 5,460 | 2,100 | 7,852 |
| 40 | a_hyperlipidemia=1 | 1,271 | 466 | 1,606 |
| 45 | a_hyperlipidemia=0 | 2,469 | 2,190 | 10,441 |
| 45 | a_hyperlipidemia=1 | 561 | 497 | 2,212 |
| 50 | a_hyperlipidemia=0 | 796 | 2,252 | 11,960 |
| 50 | a_hyperlipidemia=1 | 173 | 519 | 2,541 |
| 55 | a_hyperlipidemia=0 | 205 | 2,262 | 12,380 |
| 55 | a_hyperlipidemia=1 | 37 | 522 | 2,636 |
| 60 | a_hyperlipidemia=0 | 61 | 2,266 | 12,475 |
| 60 | a_hyperlipidemia=1 | 9 | 522 | 2,653 |
| 65 | a_hyperlipidemia=0 | 10 | 2,268 | 12,525 |
| 65 | a_hyperlipidemia=1 | 1 | 523 | 2,660 |
By a_hypertension
survfit.t6.a_hypertension<-survfit(Surv(time, status) ~ a_hypertension,
data = r.hospitalised.death)
ggsurv.t6.a_hypertension<- list(
ggsurvplot(survfit.t6.a_hypertension, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t6.a_hypertension, fun = "cloglog", conf.int =TRUE))
ggsurv.t6.a_hypertension <-arrange_ggsurvplots(ggsurv.t6.a_hypertension, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t6.a_hypertension.png",ggsurv.t6.a_hypertension,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t6.a_hypertension.png")
at.risk.t6.a_hypertension<-ggsurvplot(survfit.t6.a_hypertension, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
select(time, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t6.a_hypertension,
col.names = c("Time since hospitalisation", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Time since hospitalisation | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 0 | a_hypertension=0 | 12,962 | 0 | 0 |
| 0 | a_hypertension=1 | 5,014 | 0 | 0 |
| 5 | a_hypertension=0 | 12,765 | 123 | 155 |
| 5 | a_hypertension=1 | 4,893 | 59 | 86 |
| 10 | a_hypertension=0 | 12,211 | 419 | 450 |
| 10 | a_hypertension=1 | 4,657 | 219 | 184 |
| 15 | a_hypertension=0 | 11,506 | 800 | 843 |
| 15 | a_hypertension=1 | 4,330 | 408 | 343 |
| 20 | a_hypertension=0 | 10,678 | 1,088 | 1,398 |
| 20 | a_hypertension=1 | 3,976 | 561 | 570 |
| 25 | a_hypertension=0 | 9,722 | 1,294 | 2,124 |
| 25 | a_hypertension=1 | 3,569 | 663 | 852 |
| 30 | a_hypertension=0 | 8,461 | 1,459 | 3,409 |
| 30 | a_hypertension=1 | 3,138 | 756 | 1,242 |
| 35 | a_hypertension=0 | 6,933 | 1,574 | 4,847 |
| 35 | a_hypertension=1 | 2,630 | 822 | 1,689 |
| 40 | a_hypertension=0 | 4,869 | 1,673 | 6,979 |
| 40 | a_hypertension=1 | 1,862 | 893 | 2,479 |
| 45 | a_hypertension=0 | 2,192 | 1,754 | 9,294 |
| 45 | a_hypertension=1 | 838 | 933 | 3,359 |
| 50 | a_hypertension=0 | 699 | 1,812 | 10,639 |
| 50 | a_hypertension=1 | 270 | 959 | 3,862 |
| 55 | a_hypertension=0 | 181 | 1,818 | 11,012 |
| 55 | a_hypertension=1 | 61 | 966 | 4,004 |
| 60 | a_hypertension=0 | 58 | 1,821 | 11,091 |
| 60 | a_hypertension=1 | 12 | 967 | 4,037 |
| 65 | a_hypertension=0 | 10 | 1,822 | 11,140 |
| 65 | a_hypertension=1 | 1 | 969 | 4,045 |
By a_malignant_neoplasm
survfit.t6.a_malignant_neoplasm<-survfit(Surv(time, status) ~ a_malignant_neoplasm,
data = r.hospitalised.death)
ggsurv.t6.a_malignant_neoplasm<- list(
ggsurvplot(survfit.t6.a_malignant_neoplasm, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t6.a_malignant_neoplasm, fun = "cloglog", conf.int =TRUE))
ggsurv.t6.a_malignant_neoplasm <-arrange_ggsurvplots(ggsurv.t6.a_malignant_neoplasm, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t6.a_malignant_neoplasm.png",ggsurv.t6.a_malignant_neoplasm,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t6.a_malignant_neoplasm.png")
at.risk.t6.a_malignant_neoplasm<-ggsurvplot(survfit.t6.a_malignant_neoplasm, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
select(time, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t6.a_malignant_neoplasm,
col.names = c("Time since hospitalisation", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Time since hospitalisation | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 0 | a_malignant_neoplasm=0 | 15,498 | 0 | 0 |
| 0 | a_malignant_neoplasm=1 | 2,478 | 0 | 0 |
| 5 | a_malignant_neoplasm=0 | 15,241 | 137 | 204 |
| 5 | a_malignant_neoplasm=1 | 2,417 | 45 | 37 |
| 10 | a_malignant_neoplasm=0 | 14,609 | 476 | 542 |
| 10 | a_malignant_neoplasm=1 | 2,259 | 162 | 92 |
| 15 | a_malignant_neoplasm=0 | 13,773 | 916 | 1,020 |
| 15 | a_malignant_neoplasm=1 | 2,063 | 292 | 166 |
| 20 | a_malignant_neoplasm=0 | 12,799 | 1,246 | 1,707 |
| 20 | a_malignant_neoplasm=1 | 1,855 | 403 | 261 |
| 25 | a_malignant_neoplasm=0 | 11,653 | 1,477 | 2,576 |
| 25 | a_malignant_neoplasm=1 | 1,638 | 480 | 400 |
| 30 | a_malignant_neoplasm=0 | 10,182 | 1,677 | 4,069 |
| 30 | a_malignant_neoplasm=1 | 1,417 | 538 | 582 |
| 35 | a_malignant_neoplasm=0 | 8,405 | 1,803 | 5,760 |
| 35 | a_malignant_neoplasm=1 | 1,158 | 593 | 776 |
| 40 | a_malignant_neoplasm=0 | 5,886 | 1,929 | 8,368 |
| 40 | a_malignant_neoplasm=1 | 845 | 637 | 1,090 |
| 45 | a_malignant_neoplasm=0 | 2,630 | 2,022 | 11,193 |
| 45 | a_malignant_neoplasm=1 | 400 | 665 | 1,460 |
| 50 | a_malignant_neoplasm=0 | 823 | 2,088 | 12,819 |
| 50 | a_malignant_neoplasm=1 | 146 | 683 | 1,682 |
| 55 | a_malignant_neoplasm=0 | 201 | 2,095 | 13,259 |
| 55 | a_malignant_neoplasm=1 | 41 | 689 | 1,757 |
| 60 | a_malignant_neoplasm=0 | 58 | 2,097 | 13,350 |
| 60 | a_malignant_neoplasm=1 | 12 | 691 | 1,778 |
| 65 | a_malignant_neoplasm=0 | 8 | 2,100 | 13,398 |
| 65 | a_malignant_neoplasm=1 | 3 | 691 | 1,787 |
By a_obesity.5y
survfit.t6.a_obesity.5y<-survfit(Surv(time, status) ~ a_obesity.5y,
data = r.hospitalised.death)
ggsurv.t6.a_obesity.5y<- list(
ggsurvplot(survfit.t6.a_obesity.5y, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t6.a_obesity.5y, fun = "cloglog", conf.int =TRUE))
ggsurv.t6.a_obesity.5y <-arrange_ggsurvplots(ggsurv.t6.a_obesity.5y, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t6.a_obesity.5y.png",ggsurv.t6.a_obesity.5y,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t6.a_obesity.5y.png")
at.risk.t6.a_obesity.5y<-ggsurvplot(survfit.t6.a_obesity.5y, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
select(time, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t6.a_obesity.5y,
col.names = c("Time since hospitalisation", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Time since hospitalisation | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 0 | a_obesity.5y=0 | 11,320 | 0 | 0 |
| 0 | a_obesity.5y=1 | 6,656 | 0 | 0 |
| 5 | a_obesity.5y=0 | 11,108 | 119 | 164 |
| 5 | a_obesity.5y=1 | 6,550 | 63 | 77 |
| 10 | a_obesity.5y=0 | 10,581 | 409 | 442 |
| 10 | a_obesity.5y=1 | 6,287 | 229 | 192 |
| 15 | a_obesity.5y=0 | 9,904 | 754 | 815 |
| 15 | a_obesity.5y=1 | 5,932 | 454 | 371 |
| 20 | a_obesity.5y=0 | 9,163 | 1,010 | 1,325 |
| 20 | a_obesity.5y=1 | 5,491 | 639 | 643 |
| 25 | a_obesity.5y=0 | 8,335 | 1,175 | 1,969 |
| 25 | a_obesity.5y=1 | 4,956 | 782 | 1,007 |
| 30 | a_obesity.5y=0 | 7,232 | 1,334 | 3,060 |
| 30 | a_obesity.5y=1 | 4,367 | 881 | 1,591 |
| 35 | a_obesity.5y=0 | 5,948 | 1,441 | 4,285 |
| 35 | a_obesity.5y=1 | 3,615 | 955 | 2,251 |
| 40 | a_obesity.5y=0 | 4,141 | 1,536 | 6,119 |
| 40 | a_obesity.5y=1 | 2,590 | 1,030 | 3,339 |
| 45 | a_obesity.5y=0 | 1,896 | 1,617 | 8,064 |
| 45 | a_obesity.5y=1 | 1,134 | 1,070 | 4,589 |
| 50 | a_obesity.5y=0 | 621 | 1,661 | 9,206 |
| 50 | a_obesity.5y=1 | 348 | 1,110 | 5,295 |
| 55 | a_obesity.5y=0 | 157 | 1,671 | 9,542 |
| 55 | a_obesity.5y=1 | 85 | 1,113 | 5,474 |
| 60 | a_obesity.5y=0 | 41 | 1,672 | 9,613 |
| 60 | a_obesity.5y=1 | 29 | 1,116 | 5,515 |
| 65 | a_obesity.5y=0 | 8 | 1,673 | 9,647 |
| 65 | a_obesity.5y=1 | 3 | 1,118 | 5,538 |
By a_t2_diabetes
survfit.t6.a_t2_diabetes<-survfit(Surv(time, status) ~ a_t2_diabetes,
data = r.hospitalised.death)
ggsurv.t6.a_t2_diabetes<- list(
ggsurvplot(survfit.t6.a_t2_diabetes, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t6.a_t2_diabetes, fun = "cloglog", conf.int =TRUE))
ggsurv.t6.a_t2_diabetes <-arrange_ggsurvplots(ggsurv.t6.a_t2_diabetes, print = FALSE, ncol = 1, nrow = 2)
ggsave( "ggsurv.t6.a_t2_diabetes.png",ggsurv.t6.a_t2_diabetes,
dpi=300,
width = 9, height = 14)
include_graphics("ggsurv.t6.a_t2_diabetes.png")
at.risk.t6.a_t2_diabetes<-ggsurvplot(survfit.t6.a_t2_diabetes, fun = "event",
break.time.by=5)$data.survtable %>%
select(strata, time, n.risk, cum.n.event,cum.n.censor) %>%
mutate(n.risk=nice.num(n.risk),
cum.n.event=nice.num(cum.n.event),
cum.n.censor=nice.num(cum.n.censor)) %>%
arrange(time) %>%
select(time, strata, n.risk, cum.n.event, cum.n.censor)
kable(at.risk.t6.a_t2_diabetes,
col.names = c("Time since hospitalisation", "Strata", "Number at risk", "Cumulative events", "Cumulative censored")) %>%
kable_styling(bootstrap_options = c("striped", "bordered"))| Time since hospitalisation | Strata | Number at risk | Cumulative events | Cumulative censored |
|---|---|---|---|---|
| 0 | a_t2_diabetes=0 | 14,786 | 0 | 0 |
| 0 | a_t2_diabetes=1 | 3,190 | 0 | 0 |
| 5 | a_t2_diabetes=0 | 14,532 | 130 | 209 |
| 5 | a_t2_diabetes=1 | 3,126 | 52 | 32 |
| 10 | a_t2_diabetes=0 | 13,917 | 467 | 534 |
| 10 | a_t2_diabetes=1 | 2,951 | 171 | 100 |
| 15 | a_t2_diabetes=0 | 13,104 | 883 | 993 |
| 15 | a_t2_diabetes=1 | 2,732 | 325 | 193 |
| 20 | a_t2_diabetes=0 | 12,183 | 1,198 | 1,638 |
| 20 | a_t2_diabetes=1 | 2,471 | 451 | 330 |
| 25 | a_t2_diabetes=0 | 11,105 | 1,421 | 2,455 |
| 25 | a_t2_diabetes=1 | 2,186 | 536 | 521 |
| 30 | a_t2_diabetes=0 | 9,724 | 1,612 | 3,849 |
| 30 | a_t2_diabetes=1 | 1,875 | 603 | 802 |
| 35 | a_t2_diabetes=0 | 8,026 | 1,752 | 5,445 |
| 35 | a_t2_diabetes=1 | 1,537 | 644 | 1,091 |
| 40 | a_t2_diabetes=0 | 5,674 | 1,885 | 7,876 |
| 40 | a_t2_diabetes=1 | 1,057 | 681 | 1,582 |
| 45 | a_t2_diabetes=0 | 2,558 | 1,978 | 10,588 |
| 45 | a_t2_diabetes=1 | 472 | 709 | 2,065 |
| 50 | a_t2_diabetes=0 | 799 | 2,042 | 12,172 |
| 50 | a_t2_diabetes=1 | 170 | 729 | 2,329 |
| 55 | a_t2_diabetes=0 | 198 | 2,051 | 12,592 |
| 55 | a_t2_diabetes=1 | 44 | 733 | 2,424 |
| 60 | a_t2_diabetes=0 | 61 | 2,055 | 12,678 |
| 60 | a_t2_diabetes=1 | 9 | 733 | 2,450 |
| 65 | a_t2_diabetes=0 | 10 | 2,057 | 12,729 |
| 65 | a_t2_diabetes=1 | 1 | 734 | 2,456 |